Methods and systems for generating a descriptor trail using artificial intelligence

ABSTRACT

A system for generating a descriptor trail using artificial intelligence. The system includes at least a server configured to receive at least a biological extraction. At least a server is configured to generate a prognostic output as a function of at least a biological extraction. At least a server is configured to generate an ameliorative output as a function of a prognostic output. The system includes a descriptor generator module operating on at least a server. A descriptor generator module is configured to generate at least a descriptor trail from a descriptor trail data structure wherein the descriptor trail further comprises at least an element of diagnostic data.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for generating a descriptor trail using artificialintelligence.

BACKGROUND

Acquiring trust in computing proves to be challenging. Analyzing largequantities of data and ensuring accurate utilization of data can bedifficult. Incorrect analysis and utilization can frustrate users andcause a lack of trustworthiness.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for generating a descriptor trail usingartificial intelligence the system comprising at least a server. Atleast a server designed and configured to receive at least a biologicalextraction. At least a server is designed and configured to generate aprognostic output as a function of the biological extraction, whereingenerating the prognostic output further comprises selecting aprognostic machine-learning process as a function of the biologicalextraction recording the selected prognostic machine-learning process ina descriptor trail data structure; and generating the prognostic outputusing the selected prognostic machine-learning process as a function ofthe biological extraction. At least a server is designed and configuredto generate an ameliorative output as a function of the prognosticoutput, wherein generating the ameliorative output further comprisesselecting an ameliorative machine-learning process as a function of theprognostic label, recording the selected ameliorative machine-learningprocess in the descriptor trail data structure; and generating theameliorative output using the selected ameliorative machine-learningprocess as a function of the prognostic output. The system includes adescriptor generator module operating on the at least a serve whereinthe descriptor generator module is designed and configured to generateat least a descriptor trail from the descriptor trail data structurewherein the at least a descriptor trail further comprises at least anelement of diagnostic data.

In an aspect, a method of generating a descriptor trail using artificialintelligence the method comprising receiving by at least a server atleast a biological extraction. The method includes generating by the atleast a server a prognostic output as a function of the biologicalextraction, wherein generating the prognostic output further comprisesselecting a prognostic machine-learning process as a function of thebiological extraction, recording the selected prognosticmachine-learning process in a descriptor trail data structure; andgenerating the prognostic output using the selected prognosticmachine-learning process as a function of the biological extraction. Themethod includes generating by the at least a server an ameliorativeoutput as a function of the prognostic output, wherein generating theameliorative output further comprises selecting an ameliorativemachine-learning process as a function of the prognostic label,recording the selected ameliorative machine-learning process in thedescriptor trail data structure; and generating the ameliorative outputusing the selected ameliorative machine-learning process as a functionof the prognostic output. The method includes generating by the at leasta server at least a descriptor trail from the descriptor trail datastructure wherein the at least a descriptor trail further comprises atleast an element of diagnostic data.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for generating a descriptor trail using artificial intelligence;

FIG. 2 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 3 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of aprognostic label database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anameliorative process label database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of abiological extraction classifier label database;

FIG. 8 is a block diagram illustrating an exemplary embodiment of aphysiological categories database;

FIG. 9 is a block diagram illustrating an exemplary embodiment of atraining set database;

FIG. 10 is a block diagram illustrating an exemplary embodiment of aprognostic label learner;

FIG. 11 is a block diagram illustrating an exemplary embodiment of anameliorative process label learner;

FIG. 12 is a block diagram illustrating an exemplary embodiment of adescriptor generator module;

FIG. 13 is a block diagram illustrating an exemplary embodiment of aprognostic label classification database;

FIG. 14 is a block diagram illustrating an exemplary embodiment of anarrative language database;

FIG. 15 is a block diagram illustrating an exemplary embodiment of animage database;

FIG. 16 is a block diagram illustrating an exemplary embodiment of adescriptor trail data structure;

FIG. 17 is a process flow diagram illustrating an exemplary embodimentof a method for generating a descriptor trail using artificialintelligence; and

FIG. 18 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for generating a descriptor trail using artificialintelligence. In an embodiment, at least a server receives at least abiological extraction. At least a server generates a prognostic outputas a function of at least a biological extraction. At least a servergenerates an ameliorative output as a function of a prognostic output.At least a server records descriptor trail data such as a prognosticmachine-learning process or an ameliorative machine-learning process ina descriptor trail data structure. At least a server generates at leasta descriptor trail wherein the at least a descriptor trail includes atleast an element of diagnostic data. At least a server filters adescriptor trail as a function of an advisory input.

Referring now to the drawings, FIG. 1 illustrates an exemplaryembodiment of a system 100 for generating a descriptor trail usingartificial intelligence. System 100 includes at least a server 104. Atleast a server 104 may include any computing device as described herein,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described below.At least a server 104 may be housed with, may be incorporated in, or mayincorporate one or more sensor of at least a sensor. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. At least a server 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. At least a server 104 may include one or more additionaldevices as described below in further detail via a network interfacedevice. Network interface device may be utilized for connecting a atleast a server 104 to one or more of a variety of networks, and one ormore devices. Examples of a network interface device include, but arenot limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. At least a server 104 may include but is not limited to, forexample, at least a server 104 or cluster of computing devices in afirst location and a second computing device or cluster of computingdevices in a second location. At least a server 104 may include one ormore computing devices dedicated to data storage, security, distributionof traffic for load balancing, and the like. At least a server 104 maydistribute one or more computing tasks as described below across aplurality of computing devices, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. At least a server 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

With continued reference to FIG. 1 , at least a server 104 and/or one ormore modules operating thereon may be designed and/or configured toperform any method, method step, or sequence of method steps in anyembodiment described in this disclosure, in any order and with anydegree of repetition. For instance, at least a server 104 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. At least a server 104and/or one or more modules operating thereon may perform any step orsequence of steps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , at least a server 104 is configuredto receive at least a biological extraction. At least a biologicalextraction may include any element and/or elements of data suitable foruse as at least an element of physiological state data 124 as describedin more detail below. At least a biological extraction may include aphysically extracted sample, which as used herein, includes a sampleobtained by removing and analyzing tissue and/or fluid. Physicallyextracted sample may include without limitation a blood sample, a tissuesample, a buccal swab, a mucous sample, a stool sample, a hair sample, afingernail sample, or the like. Physically extracted sample may include,as a non-limiting example, at least a blood sample. As a furthernon-limiting example, at least a biological extraction may include atleast a genetic sample. At least a genetic sample may include a completegenome of a person or any portion thereof. At least a genetic sample mayinclude a DNA sample and/or an RNA sample. At least a biologicalextraction may include an epigenetic sample, a proteomic sample, atissue sample, a biopsy, and/or any other physically extracted sample.At least a biological extraction may include an endocrinal sample. As afurther non-limiting example, the at least a biological extraction mayinclude a signal from at least a sensor 108 configured to detectphysiological data of a user and recording the at least a biologicalextraction as a function of the signal. At least a sensor 108 mayinclude any medical sensor 108 and/or medical device configured tocapture sensor 108 data concerning a patient, including any scanning,radiological and/or imaging device such as without limitation x-rayequipment, computer assisted tomography (CAT) scan equipment, positronemission tomography (PET) scan equipment, any form of magnetic resonanceimagery (MRI) equipment, ultrasound equipment, optical scanningequipment such as photo-plethysmographic equipment, or the like. Atleast a sensor 108 may include any electromagnetic sensor 108, includingwithout limitation electroencephalographic sensor 108,magnetoencephalographic sensor 108, electrocardiographic sensor 108,electromyographic sensor 108, or the like. At least a sensor 108 mayinclude a temperature sensor 108. At least a sensor 108 may include anysensor 108 that may be included in a mobile device and/or wearabledevice, including without limitation a motion sensor 108 such as aninertial measurement unit (IMU), one or more accelerometers, one or moregyroscopes, one or more magnetometers, or the like. At least a wearableand/or mobile device sensor 108 may capture step, gait, and/or othermobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor 108may detect heart rate or the like. At least a sensor 108 may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, blood sugar, and/or blood pressure. At least asensor 108 may be configured to detect internal and/or externalbiomarkers and/or readings. At least a sensor 108 may be a part ofsystem 100 or may be a separate device in communication with system 100.

Still referring to FIG. 1 , at least a biological extraction may includeany data suitable for use as physiological state data 124 as describedbelow, including without limitation any result of any medical test,physiological assessment, cognitive assessment, psychologicalassessment, or the like. System 100 may receive at least a biologicalextraction from one or more other devices after performance; system 100may alternatively or additionally perform one or more assessments and/ortests to obtain at least a biological extraction, and/or one or moreportions thereof, on system 100. For instance, at least biologicalextraction may include or more entries by a user in a form or similargraphical user interface object; one or more entries may include,without limitation, user responses to questions on a psychological,behavioral, personality, or cognitive test. For instance, at least aserver 104 may present to user a set of assessment questions designed orintended to evaluate a current state of mind of the user, a currentpsychological state of the user, a personality trait of the user, or thelike; at least a server 104 may provide user-entered responses to suchquestions directly as at least a biological extraction and/or mayperform one or more calculations or other algorithms to derive a scoreor other result of an assessment as specified by one or more testingprotocols, such as automated calculation of a Stanford-Binet and/orWechsler scale for IQ testing, a personality test scoring such as aMyers-Briggs test protocol, or other assessments that may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure.

With continued reference to FIG. 1 , at least a biological extractionmay include assessment and/or self-assessment data, and/or automated orother assessment results, obtained from a third-party device;third-party device may include, without limitation, a server or otherdevice (not shown) that performs automated cognitive, psychological,behavioral, personality, or other assessments. Third-party device mayinclude a device operated by an informed advisor.

Still referring to FIG. 1 , at least a biological extraction may includedata describing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensor 108 tests. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of at least a physiological sample consistent withthis disclosure.

With continued reference to FIG. 1 , at least a server 104 may beconfigured to receive at least a biological extraction wherein the atleast a biological extraction further comprises a fluid sample andclassify the at least a biological extraction. Fluid sample may includeany of the fluid samples as described above. Fluid sample may includefor example, a blood sample, a urine sample, a semen sample, a sweatsample, a cerebrospinal fluid sample, a pleural fluid sample, a salivasample, a synovial fluid sample, a pleural fluid sample, a pericardialfluid sample, a peritoneal fluid sample, and the like. Classifying theat least a biological extraction may include comparing the at least abiological extraction to at least a biological standard level andgenerating at least a biological extraction classifier label. Abiological standard level, as used herein, includes any reference rangefor a given fluid sample that may include values utilized to interpretthe results of a fluid sample. Reference range may include a set ofvalues that approximately ninety five percent of the population may fallwithin. In an embodiment, biological standard level may include anyreference range and/or control data for any biological extraction,including for example an image such as an x-ray, a user describedbehavior such as a response to a questionnaire about mental health of auser, and/or a genetic sample. Reference ranges may be reported byconcentration such as by mass, by molarity, by units, by enzymeactivity, by white blood cell count, by absence, by presence, and thelike. Reference ranges may be created and/or endorsed in practice byparticular medical groups and associations such as for example THEAMERICAN MEDICAL ASSOCIATION of Chicago, Ill., THE AMERICAN ASSOCIATIONOF CLINICAL ENDOCINOLOGISTS of Jacksonville, Fla., THE INSTITUTE FORFUNCTIONAL MEDICINE of Federal Way, Wash., THE AMERICAN ACADEMY OFANTI-AGING MEDICINE of Chicago, Ill. and the like. For example, a fluidsample such as a blood fluid sample analyzed for blood calcium levelsmay be associated with a reference range between 8.6 to 10.3 mg/dL(milligrams per deciliter). In yet another non-limiting example, a fluidsample such as a urine sample analyzed for glucose levels may beassociated with a reference range between 0 to 0.8 mmol/L (millimolesper liter). In an embodiment, reference range may consist of controldata that may indicate whether a particular biological extractioncontains abnormal findings such as whether a particular x-ray contains abroken bone or whether a particular answer on a questionnaire indicatesa possibility of depression.

With continued reference to FIG. 1 at least a biological extraction maybe compared to at least a biological standard level and a biologicalextraction classifier label may be generated. A biological extractionclassifier label, as used herein, is a datum generated by comparing atleast a biological extraction to at least a biological standard level.Biological extraction classifier label may indicate how far or how closeto any given reference range a particular biological extraction is byclassifying a particular biological extraction as compared to areference range. Classifications may include a biological extractionclassifier label of “normal” when a biological extraction falls within agiven reference range. Classifications may include a biologicalextraction classifier label of “elevated” when a biological extractionfalls above a given reference range. Classifications may include abiological extraction classifier label of “low” when a biologicalextraction falls below a given reference range. Classifications mayinclude a biological extraction classifier label of “abnormal” when abiological extraction falls above and/or below a given reference range.For example, at least a biological extraction such as a blood samplecontaining a thyroid stimulating hormone (TSH) level of 12.5milli-international units per liter may contain a biological extractionclassifier label of “elevated” as compared to a reference range between0.4 to 4.0 milli-international units per liter. In yet anothernon-limiting example, at least a biological extraction such as a urinesample containing a urea nitrogen level of 11.2 milligrams per deciliter(mg/dL) may contain a biological extraction classifier label of “normal”as compared to a reference range between 7 to 20 mg/dL. In anembodiment, at least a biological extraction may contain a plurality ofbiological extraction classifier labels. For instance and withoutlimitation, at least a biological extraction such as a cerebrospinalfluid sample containing a protein level of 6.0 milligrams per 100milters may contain biological extraction classifier labels of “low” and“abnormal” as compared to a reference range between 15 to 60 milligramsper 100 milliliters.

With continued reference to FIG. 1 , generating at least a biologicalextraction classifier label may include matching at least a biologicalextraction with at least a category of physiological state data receivedfrom at least an expert. For instance and without limitation, contentsof at least a biological extraction may be analyzed to determineparticular fluid sample and analysis to match a given category ofphysiological data, and/or a given reference range of physiologicaldata. For example, at least a biological extraction containing a bloodsample containing a chem-7 basic metabolic panel may be matched to agiven category of physiological data that relates to metabolic panels.In yet another non-limiting example, at least a biological extractioncontaining a urinalysis examining urine glucose levels may be matched toa given category of physiological data that relates to diagnosis ofdiabetes based on urine glucose levels. In such an instance, urineglucose levels may be matched to a given category of physiological datathat may aid in evaluating urine glucose levels against referenceranges. Categories of physiological data received from at least anexpert may include any of the categories of physiological data andexpert input as described in more detail below.

With continued reference to FIG. 1 , at least a server is configured togenerate a prognostic output as a function of a biological extraction.Generating a prognostic output includes selecting a prognosticmachine-learning process as a function of a biological extraction,recording the selected prognostic machine-learning process in adescriptor trail data structure and generating the prognostic outputusing the selected prognostic machine-learning process as a function ofthe biological extraction.

With continued reference to FIG. 1 , generating a prognostic output maybe performed by a prognostic label learner operating on at least aserver 104. With continued reference to FIG. 1 , at least a server 104includes a prognostic label learner 112 operating on the at least aserver, the prognostic label learner 112 designed and configured togenerate at least a prognostic output wherein generating the at least aprognostic output further comprises creating at least a prognosticmachine-learning model 116 relating physiological state data 124 toprognostic labels using at least a first training set 120 and generatingthe at least a prognostic output using the at least a biologicalextraction, the at least a first training set, and the at least aprognostic machine-learning model. Prognostic label learner 112 mayinclude any hardware and/or software module. Prognostic label learner112 is designed and configured to generate at least a prognostic outputusing machine-learning processes. A machine-learning process is aprocess that automatedly uses a body of data known as “training data”and/or a “training set” to generate an algorithm that will be performedby a computing device and/or module to produce outputs given dataprovided as inputs; this is in contrast to a non-machine-learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language.

Still referring to FIG. 1 , prognostic label learner 112 may be designedand configured to generate at least a prognostic output by creating atleast a prognostic machine-learning model 116 relating physiologicalstate data 124 to prognostic labels using a first training set andgenerating the at least a prognostic output using the prognosticmachine-learning model 116; at least a prognostic machine-learning model116 may include one or more models that determine a mathematicalrelationship between physiological state data 124 and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

With continued reference to FIG. 1 , at least a server 104 and/orprognostic label learner 112 may select a prognostic machine-learningalgorithm as a function of the at least a biological extraction. Forexample, a biological extraction such as a hair sample may be bestsuited for a particular machine-learning algorithm such as ahierarchical clustering model while a biological extraction such as ablood sample may be best suited for a particular machine-learning modelsuch as a supervised machine-learning model. In an embodiment,biological extractions may be matched to machine-learning algorithms. Inan embodiment, a first training set selected as a function of at least abiological extraction may be best suited for a particularmachine-learning algorithm. For instance and without limitation, abiological extraction such as a tissue sample analysis that is utilizedto select a first training set that contains a plurality of three dataentries may be best suited for a supervised machine-learning algorithmwhile a biological extraction such as a blood sample analysis that isutilized to select a first training set that contains a plurality ofthree hundred data entries may be best suited for an unsupervisedmachine-learning clustering algorithm whereby clusters generated fromthe unsupervised machine-learning algorithm may then be utilized in asupervised machine-learning algorithm. In an embodiment, at least aserver 104 and/or prognostic label learner 112 may select amachine-learning model that relates particular inputs to outputs. Forexample, at least a server 104 and/or prognostic label learner 112 mayselect a machine-learning model as a function of the sample containedwithin at least a biological extraction whereby a blood sample may beutilized to select a particular machine-learning model and a urinesample may be utilized to select a separate machine-learning model.

With continued reference to FIG. 1 , machine-learning algorithms maygenerate prognostic output as a function of a classification of at leasta prognostic label. Classification as used herein includes pairing orgrouping prognostic labels as a function of a shared commonality.Classification may include for example, groupings, pairings, and/ortrends between physiological data and current prognostic label, futureprognostic label, and the like. In an embodiment, machine-learningalgorithms may examine relationships between a future propensity of auser to develop a condition based on current user physiological data.Machine-learning algorithms may include any and all algorithms asperformed by any modules, described herein for prognostic label learner112. For example, machine-learning algorithms may relate fasting bloodglucose readings of a user to user's future propensity to developdiabetes. Machine-learning algorithms may examine precursor conditionand future propensity to develop a subsequent disorder. For example,machine-learning algorithms may examine a user diagnosed with chickenpox and user's future propensity to subsequently develop shingles. Inyet another non-limiting example, machine-learning algorithms mayexamine infection with human papillomavirus (HPV) and subsequent cancerdiagnosis. Machine-learning algorithms may examine a user's propensityto have recurring attacks of a disease or condition, for example a userwith elevated uric acid levels and repeated attacks of gout.Machine-learning algorithms may examine user's genetic predisposition todevelop a certain condition or disease. For example, machine-learningalgorithms may examine presence of hereditary non-polyposis colorectalcancer (HNPCC) commonly known as lynch syndrome, and subsequentdiagnosis of colorectal cancer. In yet another non-limiting example,machine-learning algorithms may examine presence of abnormal squamouscells and/or abnormal glandular cells in the cervix and subsequentdevelopment of cervical cancer. Machine-learning algorithms may examineprogression of disease state, for example progression of humanimmunodeficiency virus (HIV) is marked by decline of CD4+ T-Cells, witha count below 200 leading to a diagnosis of acquired immunodeficiencysyndrome (AIDS). In yet another non-limiting example, progression ofdiabetes may be marked by increases of hemoglobin A1C levels with alevel of 6.5% indicating a diagnosis of diabetes. Machine-learningalgorithms may examine progression of disease by certain age groups. Forexample, progression of Multiple Sclerosis in users between the age of20-30 as compared to progression of Multiple Sclerosis in users betweenthe age of 70-80. Machine-learning algorithms may be examiningprogression of aging such as measurements of telomere length and/oroxidative stress levels and chance mortality risk. Machine-learningalgorithms may examine development of co-morbid conditions when adisease or conditions is already present. For example, machine-learningalgorithms may examine a user diagnosed with depression and subsequentdiagnosis of a co-morbid condition such as migraines, generalizedanxiety disorder, antisocial personality disorder, agoraphobia,obsessive-compulsive disorder, drug dependence alcohol dependence,and/or panic disorder. Machine-learning algorithms may examine a user'slifetime chance of developing a certain disease or condition, such as auser's lifetime risk of heart disease, Alzheimer's disease, diabetes andthe like. Machine-learning algorithms may be grouped and implementedaccording to any of the methodologies as described below.

Continuing to refer to FIG. 1 , machine-learning algorithm used togenerate prognostic machine-learning model 116 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighbors'algorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1 , prognostic label learner 112 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using a first trainingset; the trained network may then be used to apply detectedrelationships between elements of physiological state data 124 andprognostic labels.

With continued reference to FIG. 1 , prognostic label learner 112 may beconfigured to generate a plurality of prognostic outputs each containinga ranked prognostic probability score. Prognostic probability score maybe generated as a function of at least a biological extraction, at leasta first training set, and at least a prognostic machine-learning model.A prognostic probability score, as used herein, is a mathematicalrepresentation indicating a likelihood of a particular prognostic outputassociated with at least a biological extraction. Prognostic probabilityscore may include predictive values indicating a likelihood of a givenprognostic output. In an embodiment, prognostic label learner 112 maygenerate a plurality of prognostic outputs which may each be ranked suchas for example based on a decreasing likelihood of having a particularprognostic output. For instance and without limitation, at least abiological extraction such as a blood sample showing an elevated fastingblood glucose level may be utilized by prognostic label learner 112 incombination with at least a first training set 120 and at least aprognostic machine-learning model to generate a plurality of prognosticoutputs that include diabetes mellitus type 2, pancreatitis, andCushing's syndrome, with each prognostic output including a prognosticprobability score and ranked in deceasing order of likelihood. In anembodiment, prognostic probability score may be calculated based onprevalence and predictive values. Prevalence indicates the probabilityof having a particular prognosis, and may also be known as the priorprobability of having a particular prognosis. Predictive value indicatesthe probability of a prognosis in an individual with a biologicalextraction outside of normal limits and which contains a biologicalextraction classifier label of “low,” “elevated” and/or “abnormal.”Negative predictive value indicates the probability of not having aprognosis, such as when at least a biological extraction contains abiological extraction classifier label of “normal.” In an embodiment,prognostic probability score may be calculated using machine-learningmethods which may include any of the machine-learning methods asdescribed herein. For example, in an embodiment prognostic label learner112 may generate a plurality of prognostic outputs each containing aranked prognostic probability score generated as a function of at leasta biological extraction, at least a first training set, and at least aprognostic machine-learning model. In an embodiment, generatingprognostic output may include selecting a lazy-learning process as afunction of at least a biological extraction, recording thelazy-learning process in the descriptor trail data structure, andgenerating the prognostic output using the lazy-learning process as afunction of the at least a biological extraction.

With continued reference to FIG. 1 , at least a server 104 and/orprognostic label learner 112 may be configured to select training datato generate prognostic output using a selected machine-learning process.Training data, as used herein, is data containing correlation that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1 , trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

With continued reference to FIG. 1 , at least a server 104 and/orprognostic label learner 112 may be configured to select a firsttraining set 120 including a plurality of first data entries, each firstdata entry of the first training set 120 including at least an elementof physiological state data 124 and at least a correlated firstprognostic label 128. At least an element of physiological state data124 may include any data indicative of a person's physiological state;physiological state may be evaluated with regard to one or more measuresof health of a person's body, one or more systems within a person's bodysuch as a circulatory system, a digestive system, a nervous system, orthe like, one or more organs within a person's body, and/or any othersubdivision of a person's body useful for diagnostic or prognosticpurposes. Physiological state data 124 may include, without limitation,hematological data, such as red blood cell count, which may include atotal number of red blood cells in a person's blood and/or in a bloodsample, hemoglobin levels, hematocrit representing a percentage of bloodin a person and/or sample that is composed of red blood cells, meancorpuscular volume, which may be an estimate of the average red bloodcell size, mean corpuscular hemoglobin, which may measure average weightof hemoglobin per red blood cell, mean corpuscular hemoglobinconcentration, which may measure an average concentration of hemoglobinin red blood cells, platelet count, mean platelet volume which maymeasure the average size of platelets, red blood cell distributionwidth, which measures variation in red blood cell size, absoluteneutrophils, which measures the number of neutrophil white blood cells,absolute quantities of lymphocytes such as B-cells, T-cells, NaturalKiller Cells, and the like, absolute numbers of monocytes includingmacrophage precursors, absolute numbers of eosinophils, and/or absolutecounts of basophils. Physiological state data 124 may include, withoutlimitation, immune function data such as Interleukine-6 (IL-6),TNF-alpha, systemic inflammatory cytokines, and the like.

Continuing to refer to FIG. 1 , physiological state data 124 mayinclude, without limitation, data describing blood-born lipids,including total cholesterol levels, high-density lipoprotein (HDL)cholesterol levels, low-density lipoprotein (LDL) cholesterol levels,very low-density lipoprotein (VLDL) cholesterol levels, levels oftriglycerides, and/or any other quantity of any blood-born lipid orlipid-containing substance. Physiological state data 124 may includemeasures of glucose metabolism such as fasting glucose levels and/orhemoglobin A1-C (HbA1c) levels. Physiological state data 124 mayinclude, without limitation, one or more measures associated withendocrine function, such as without limitation, quantities ofdehydroepiandrosterone (DHEAS), DHEA-Sulfate, quantities of cortisol,ratio of DHEAS to cortisol, quantities of testosterone quantities ofestrogen, quantities of growth hormone (GH), insulin-like growth factor1 (IGF-1), quantities of adipokines such as adiponectin, leptin, and/orghrelin, quantities of somatostatin, progesterone, or the like.Physiological state data 124 may include measures of estimatedglomerular filtration rate (eGFR) Physiological state data 124 mayinclude quantities of C-reactive protein, estradiol, ferritin, folate,homocysteine, prostate-specific Ag, thyroid-stimulating hormone, vitaminD, 25 hydroxy, blood urea nitrogen, creatinine, sodium, potassium,chloride, carbon dioxide, uric acid, albumin, globulin, calcium,phosphorus, alkaline phosphatase, alanine amino transferase, aspartateamino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data 124 may includeantinuclear antibody levels. Physiological state data 124 may includealuminum levels. Physiological state data 124 may include arseniclevels. Physiological state data 124 may include levels of fibrinogen,plasma cystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1 , physiological state data 124 may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data 124 may include a measurement blood pressure, includingwithout limitation systolic and diastolic blood pressure. Physiologicalstate data 124 may include a measure of waist circumference.Physiological state data 124 may include body mass index (BMI).Physiological state data 124 may include one or more measures of bonemass and/or density such as dual-energy x-ray absorptiometry.Physiological state data 124 may include one or more measures of musclemass. Physiological state data 124 may include one or more measures ofphysical capability such as without limitation measures of gripstrength, evaluations of standing balance, evaluations of gait speed,pegboard tests, timed up and go tests, and/or chair rising tests.

Still viewing FIG. 1 , physiological state data 124 may include one ormore measures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data124 may include one or more measures of psychological function or state,such as without limitation clinical interviews, assessments ofintellectual functioning and/or intelligence quotient (IQ) tests,personality assessments, and/or behavioral assessments. Physiologicalstate data 124 may include one or more psychological self-assessments,which may include any self-administered and/or automatedlycomputer-administered assessments, whether administered within system100 and/or via a third-party service or platform.

Continuing to refer to FIG. 1 , physiological state data 124 may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processingmodules as described in this disclosure.

With continued reference to FIG. 1 , physiological state data 124 mayinclude one or more evaluations of sensor 108, including measures ofaudition, vision, olfaction, gustation, vestibular function and pain.Physiological state data 124 may include genomic data, includingdeoxyribonucleic acid (DNA) samples and/or sequences, such as withoutlimitation DNA sequences contained in one or more chromosomes in humancells. Genomic data may include, without limitation, ribonucleic acid(RNA) samples and/or sequences, such as samples and/or sequences ofmessenger RNA (mRNA) or the like taken from human cells. Genetic datamay include telomere lengths. Genomic data may include epigenetic dataincluding data describing one or more states of methylation of geneticmaterial. Physiological state data 124 may include proteomic data, whichas used herein, is data describing all proteins produced and/or modifiedby an organism, colony of organisms, or system of organisms, and/or asubset thereof. Physiological state data 124 may include data concerninga microbiome of a person, which as used herein, includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data 124 of a person, and/or on prognostic labelsand/or ameliorative processes as described in further detail below.Physiological state data 124 may include any physiological state data124, as described above, describing any multicellular organism living inor on a person including any parasitic and/or symbiotic organisms livingin or on the persons; non-limiting examples may include mites,nematodes, flatworms, or the like.

With continuing reference to FIG. 1 , physiological state data 124 mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Examples of physiological state data124 described in this disclosure are presented for illustrative purposesonly and are not meant to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data 124 that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

Continuing to refer to FIG. 1 , each element of first training set 120includes at least a first prognostic label 128. A prognostic label, asdescribed herein, is an element of data identifying and/or describing acurrent, incipient, or probable future medical condition affecting aperson; medical condition may include a particular disease, one or moresymptoms associated with a syndrome, a syndrome, and/or any othermeasure of current or future health and/or heathy aging. At least aprognostic label may be associated with a physical and/or somaticcondition, a mental condition such as a mental illness, neurosis, or thelike, or any other condition affecting human health that may beassociated with one or more elements of physiological state data 124 asdescribed in further detail below. Conditions associated with prognosticlabels may include, without limitation one or more diseases, defined forpurposes herein as conditions that negatively affect structure and/orfunction of part or all of an organism. Conditions associated withprognostic labels may include, without limitation, acute or chronicinfections, including without limitation infections by bacteria,archaea, viruses, viroids, prions, single-celled eukaryotic organismssuch as amoeba, paramecia, trypanosomes, plasmodia, leishmania, and/orfungi, and/or multicellular parasites such as nematodes, arthropods,fungi, or the like. Prognostic labels may be associated with one or moreimmune disorders, including without limitation immunodeficiencies and/orauto-immune conditions. Prognostic labels may be associated with one ormore metabolic disorders. Prognostic labels may be associated with oneor more endocrinal disorders. Prognostic labels may be associated withone or more cardiovascular disorders. Prognostic labels may beassociated with one or more respiratory disorders. Prognostic labels maybe associated with one or more disorders affecting connective tissue.Prognostic labels may be associated with one or more digestivedisorders. Prognostic labels may be associated with one or moreneurological disorders such as neuromuscular disorders, dementia, or thelike. Prognostic labels may be associated with one or more disorders ofthe excretory system, including without limitation nephrologicaldisorders. Prognostic labels may be associated with one or more liverdisorders. Prognostic labels may be associated with one or moredisorders of the bones such as osteoporosis. Prognostic labels may beassociated with one or more disorders affecting joints, such asosteoarthritis, gout, and/or rheumatoid arthritis. Prognostic labels beassociated with one or more cancers, including without limitationcarcinomas, lymphomas, leukemias, germ cell tumor cancers, blastomas,and/or sarcomas. Prognostic labels may include descriptors of latent,dormant, and/or apparent disorders, diseases, and/or conditions.Prognostic labels may include descriptors of conditions for which aperson may have a higher than average probability of development, suchas a condition for which a person may have a “risk factor”; forinstance, a person currently suffering from abdominal obesity may have ahigher than average probability of developing type II diabetes. Theabove-described examples are presented for illustrative purposes onlyand are not intended to be exhaustive. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of conditions that may be associated with prognosticlabels as described in this disclosure.

Still referring to FIG. 1 , at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a prognosticlabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least a prognostic label consistently with thisdisclosure.

With continued reference to FIG. 1 , at least a first training set 120may be selected as a function of at least a biological extractionclassifier label. For instance and without limitation, at least abiological extraction classifier label that is marked as “elevated” maybe utilized to select at least a first training set 120 that includes aplurality of first data entries where at least a first data entryincludes at least an element of physiological data that includes anelement of “elevated” physiological data and at least a correlated firstprognostic label. For example, at least a biological extraction such asa fasting blood glucose level that contains at least a biologicalextraction classifier label of “elevated” may be utilized to select atleast a first training set 120 that includes at least an element ofphysiological data that includes at least an “elevated” fasting bloodglucose level and at least a correlated first prognostic label. In yetanother non-limiting example, at least a biological extraction such as ahigh density lipoprotein level (HDL) that contains at least a biologicalclassifier label of “low” may be utilized to select at least a firsttraining set 120 that includes at least an element of physiological datathat includes at least a “low” high density lipoprotein level and atleast a correlated first prognostic label.

With continued reference to FIG. 1 , in each first data element of firsttraining set 120, at least a first prognostic label of the data elementis correlated with at least an element of physiological state data 124of the data element. In an embodiment, an element of physiological datais correlated with a prognostic label where the element of physiologicaldata is located in the same data element and/or portion of data elementas the prognostic label; for example, and without limitation, an elementof physiological data is correlated with a prognostic element where bothelement of physiological data and prognostic element are containedwithin the same first data element of the first training set 120. As afurther example, an element of physiological data is correlated with aprognostic element where both share a category label as described infurther detail below, where each is within a certain distance of theother within an ordered collection of data in data element, or the like.Still further, an element of physiological data may be correlated with aprognostic label where the element of physiological data and theprognostic label share an origin, such as being data that was collectedwith regard to a single person or the like. In an embodiment, a firstdatum may be more closely correlated with a second datum in the samedata element than with a third datum contained in the same data element;for instance, the first element and the second element may be closer toeach other in an ordered set of data than either is to the thirdelement, the first element and second element may be contained in thesame subdivision and/or section of data while the third element is in adifferent subdivision and/or section of data, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various forms and/or degrees of correlation betweenphysiological data and prognostic labels that may exist in firsttraining set 120 and/or first data element consistently with thisdisclosure.

In an embodiment, and still referring to FIG. 1 , at least a server 104may be designed and configured to associate at least an element ofphysiological state data 124 with at least a category from a list ofsignificant categories of physiological state data 124. Significantcategories of physiological state data 124 may include labels and/ordescriptors describing types of physiological state data 124 that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data 124 based on degreeof diagnostic relevance to one or more impactful conditions and/orwithin one or more medical or public health fields. For instance, andwithout limitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, LDL-C and/or HDL-C may be recognized as useful inidentifying conditions such as poor thyroid function, insulinresistance, blood glucose dysregulation, magnesium deficiency,dehydration, kidney disease, familial hypercholesterolemia, liverdysfunction, oxidative stress, inflammation, malabsorption, anemia,alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease,atherosclerosis, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalcategories of physiological data that may be used consistently with thisdisclosure.

Still referring to FIG. 1 , at least a server 104 may receive the listof significant categories according to any suitable process; forinstance, and without limitation, at least a server 104 may receive thelist of significant categories from at least an expert. In anembodiment, at least a server 104 and/or a user device connected to atleast a server 104 may provide a first graphical user interface 132,which may include without limitation a form or other graphical elementhaving data entry fields, wherein one or more experts, including withoutlimitation clinical and/or scientific experts, may enter informationdescribing one or more categories of physiological data that the expertsconsider to be significant or useful for detection of conditions; fieldsin graphical user interface may provide options describing previouslyidentified categories, which may include a comprehensive ornear-comprehensive list of types of physiological data detectable usingknown or recorded testing methods, for instance in “drop-down” lists,where experts may be able to select one or more entries to indicatetheir usefulness and/or significance in the opinion of the experts.Fields may include free-form entry fields such as text-entry fieldswhere an expert may be able to type or otherwise enter text, enablingexpert to propose or suggest categories not currently recorded. Firstgraphical user interface 132 or the like may include fieldscorresponding to prognostic labels, where experts may enter datadescribing prognostic labels and/or categories of prognostic labels theexperts consider related to entered categories of physiological data;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded prognosticlabels, and which may be comprehensive, permitting each expert to selecta prognostic label and/or a plurality of prognostic labels the expertbelieves to be predicted and/or associated with each category ofphysiological data selected by the expert. Fields for entry ofprognostic labels and/or categories of prognostic labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface may provide an expert with a field in which to indicate areference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

With continued reference to FIG. 1 , data information describingsignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or significant categories ofprognostic labels may alternatively or additionally be extracted fromone or more documents using a language processing module 136. Languageprocessing module 136 may include any hardware and/or software module.Language processing module 136 may be configured to extract, from theone or more documents, one or more words. One or more words may include,without limitation, strings of one or characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic marks, engineering symbols, geometric dimensioning andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,whitespace, and other symbols, including any symbols usable as textualdata as described above. Textual data may be parsed into tokens, whichmay include a simple word (sequence of letters separated by whitespace)or more generally a sequence of characters as described previously. Theterm “token,” as used herein, refers to any smaller, individualgroupings of text from a larger source of text; tokens may be broken upby word, pair of words, sentence, or other delimitation. These tokensmay in turn be parsed in various ways. Textual data may be parsed intowords or sequences of words, which may be considered words as well.Textual data may be parsed into “n-grams”, where all sequences of nconsecutive characters are considered. Any or all possible sequences oftokens or words may be stored as “chains”, for example for use as aMarkov chain or Hidden Markov Model.

Still referring to FIG. 1 , language processing module 136 may compareextracted words to categories of physiological data recorded at least aserver 104, one or more prognostic labels recorded at least a server104, and/or one or more categories of prognostic labels recorded atleast a server 104; such data for comparison may be entered on at leasta server 104 as described above using expert data inputs or the like. Inan embodiment, one or more categories may be enumerated, to find totalcount of mentions in such documents. Alternatively or additionally,language processing module 136 may operate to produce a languageprocessing model. Language processing model may include a programautomatically generated by at least a server 104 and/or languageprocessing module 136 to produce associations between one or more wordsextracted from at least a document and detect associations, includingwithout limitation mathematical associations, between such words, and/orassociations of extracted words with categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels. Associations between language elements, wherelanguage elements include for purposes herein extracted words,categories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels may include,without limitation, mathematical associations, including withoutlimitation statistical correlations between any language element and anyother language element and/or language elements. Statisticalcorrelations and/or mathematical associations may include probabilisticformulas or relationships indicating, for instance, a likelihood that agiven extracted word indicates a given category of physiological data, agiven relationship of such categories to prognostic labels, and/or agiven category of prognostic labels. As a further example, statisticalcorrelations and/or mathematical associations may include probabilisticformulas or relationships indicating a positive and/or negativeassociation between at least an extracted word and/or a given categoryof physiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels;positive or negative indication may include an indication that a givendocument is or is not indicating a category of physiological data,relationship of such category to prognostic labels, and/or category ofprognostic labels is or is not significant. For instance, and withoutlimitation, a negative indication may be determined from a phrase suchas “telomere length was not found to be an accurate predictor of overalllongevity,” whereas a positive indication may be determined from aphrase such as “telomere length was found to be an accurate predictor ofdementia,” as an illustrative example; whether a phrase, sentence, word,or other textual element in a document or corpus of documentsconstitutes a positive or negative indicator may be determined, in anembodiment, by mathematical associations between detected words,comparisons to phrases and/or words indicating positive and/or negativeindicators that are stored in memory at least a server 104, or the like.

Still referring to FIG. 1 , language processing module 136 and/or atleast a server 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used herein,are statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 136may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1 , generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1 , language processing module 136 may use acorpus of documents to generate associations between language elementsin a language processing module 136, and at least a server 104 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. In an embodiment, at least a server 104 may performthis analysis using a selected set of significant documents, such asdocuments identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface as described above inreference to FIG. 9 , or may communicate identities of significantdocuments according to any other suitable method of electroniccommunication, or by providing such identity to other persons who mayenter such identifications into at least a server 104. Documents may beentered into at least a server 104 by being uploaded by an expert orother persons using, without limitation, file transfer protocol (FTP) orother suitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, at least a server 104 may automatically obtain the documentusing such an identifier, for instance by submitting a request to adatabase or compendium of documents such as JSTOR as provided by IthakaHarbors, Inc. of New York.

Continuing to refer to FIG. 1 , whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface, alternative submissionmeans, and/or extracted from a document or body of documents asdescribed above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of physiological sample, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 1 , at least a server 104 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 1 , in an embodiment, at least a server 104may be configured, for instance as part of receiving the first trainingset 120, to associate at least correlated first prognostic label 128with at least a category from a list of significant categories ofprognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult at least a server 104 may modify list of significant categoriesto reflect this difference.

With continued reference to FIG. 1 , system 100 includes descriptortrail data structure 140. Descriptor trail data structure may beimplemented, without limitation, as a relational database, a key-valueretrieval datastore such as a NOSQL database, or any other format orstructure for use as a datastore that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure.Descriptor trail data structure may contain data tables that may storediagnostic data as described in more detail below. Descriptor trail datastructure may include any data structure capable of storing diagnosticdetail including, for example and without limitation, linked lists, hashtables, vectors, and the like.

With continued reference to FIG. 1 , at least a server 104 is configuredto generate an ameliorative output as a function of a prognostic output.Generating an ameliorative output includes selecting at least anameliorative machine-learning process as a function of a prognosticlabel, recording the selected ameliorative machine-learning process inthe descriptor trail data structure, and generating the ameliorativeoutput using the selected ameliorative machine-learning process as afunction of the prognostic output.

With continued reference to FIG. 1 , generating an ameliorative outputmay be performed by an ameliorative label learner operating on at leasta server 104. Ameliorative process label learner 144 may include anyhardware or software module suitable for use as a prognostic labellearner 112 as described above. Ameliorative process label learner 144is a machine-learning module as described above; ameliorative processlabel learner 144 may perform any machine-learning process orcombination of processes suitable for use by a prognostic label learner112 as described above. For instance, and without limitation, andameliorative process label learner 144 may be configured to create aameliorative machine-learning model 148 relating prognostic labels toameliorative labels using the second training set 152 and generate theat least an ameliorative output using the ameliorative machine-learningmodel 148; ameliorative machine-learning model 148 may be generatedaccording to any process, process steps, or combination of processesand/or process steps suitable for creation of prognosticmachine-learning model. In an embodiment, ameliorative process labellearner 144 may use data from first training set 120 as well as datafrom second training set 152; for instance, ameliorative process labellearner 144 may use lazy learning and/or model generation to determinerelationships between elements of physiological data, in combinationwith or instead of prognostic labels, and ameliorative labels. Whereameliorative process label learner 144 determines relationships betweenelements of physiological data and ameliorative labels directly, thismay determine relationships between prognostic labels and ameliorativelabels as well owing to the existence of relationships determined byprognostic label learner 112. In an embodiment, generating anameliorative output may include selecting a lazy-learning process as afunction of at least a biological extraction, recording thelazy-learning process in a descriptor trail data structure, andgenerating the prognostic output using the lazy-learning process as afunction of the at least a biological extraction.

With continued reference to FIG. 1 , ameliorative process label learner144 may be configured to generate a plurality of ameliorative outputseach containing a prognostic improvement score correlated to at least aprognostic output. A prognostic improvement score, as used herein,includes a mathematical representation indicating a likelihood of aparticular ameliorative output treating, preventing, and/or reversing agiven prognostic output. Prognostic improvement score may be generatedas a function of at least a prognostic output, at least a secondtraining set, and at least an ameliorative machine-learning model.Ameliorative process label learner 144 may generate a plurality ofameliorative outputs each containing a prognostic improvement score. Inan embodiment, plurality of ameliorative outputs may be ranked indescending order of improve ent. For example, a prognostic output suchas diabetes may be utilized by ameliorative process label learner 144 incombination with second training set, and ameliorative machine-learningmodels to generate a plurality of ameliorative process labels thatinclude intensive exercise therapy, an oral glucose controllingmedication, and a blood sugar controlling supplement which may eachcontain a prognostic improvement score indicating ability of eachparticular ameliorative output to treat, and/or reverse prognostic labelof diabetes. In such an instance, plurality of ameliorative outputsgenerated by ameliorative process label learner 144 may be ranked indescending order of prognostic improvement score whereby intensiveexercise may have the highest prognostic improvement score, oral glucosecontrolling medication may have the next highest prognostic improvementscore, and blood sugar controlling supplement may have the lowestprognostic improvement score.

With continued reference to FIG. 1 , selecting an ameliorativemachine-learning process to generate ameliorative output may includeselecting a second training set by at least a server 104 and/orameliorative label learner. Second training set 152 including aplurality of second data entries. Each second data entry of the secondtraining set 152 includes at least a second prognostic label; at least asecond prognostic label 13 6 may include any label suitable for use asat least a first prognostic label 128 as described above. Each seconddata entry of the second training set 152 includes at least anameliorative process label 160 correlated with the at least a secondprognostic label, where correlation may include any correlation suitablefor correlation of at least a first prognostic label 128 to at least anelement of physiological data as described above. As used herein, anameliorative process label 160 is an identifier, which may include anyform of identifier suitable for use as a prognostic label as describedabove, identifying a process that tends to improve a physical conditionof a user, where a physical condition of a user may include, withoutlimitation, any physical condition identifiable using a prognosticlabel. Ameliorative processes may include, without limitation, exerciseprograms, including amount, intensity, and/or types of exerciserecommended. Ameliorative processes may include, without limitation,dietary or nutritional recommendations based on data includingnutritional content, digestibility, or the like. Ameliorative processesmay include one or more medical procedures. Ameliorative processes mayinclude one or more physical, psychological, or other therapies.Ameliorative processes may include one or more medications. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various processes that may be used as ameliorative processesconsistently with this disclosure.

With continued reference to FIG. 1 , ameliorative process label learner144 and/or at least a server 104 may be configured to select anameliorative machine-learning algorithm as a function of the prognosticlabel. Selecting an ameliorative machine-learning algorithm may includeselecting a machine-learning algorithm to generate ameliorative model.For instance and without limitation, a first training set utilized togenerate a prognostic output may be correlated to a second training setthat may be utilized to generate ameliorative output which may beutilized to select an ameliorative machine-learning process. In yetanother non-limiting example, a plurality of prognostic labels generatedby prognostic label learner 112 may be utilized to select a particularmachine-learning algorithm that will utilize the plurality of prognosticlabels to generate a particular ameliorative output. In yet anothernon-limiting example, a prognostic output that contains very fewameliorative outputs such as a rare form of cancer may be utilized toselect an unsupervised machine-learning algorithm which may be bestsuited such as a k-nearest neighbors algorithm to find potentialameliorative treatment options. In yet another non-limiting example, aprognostic output that may be associated with numerous treatment optionssuch as diabetes may be best suited for a supervised machine-learningalgorithm. Ameliorative process label learner 144 and/or at least aserver 104 may select at least a machine-learning model as a function ofinputs and outputs utilized by ameliorative process label to generateameliorative model 148. For example, a particular prognostic outpututilized as an input by ameliorative process label learner 144 may bebest suited for a particular machine-learning model. In yet anothernon-limiting example, a particular desired ameliorative output that willbe generated by ameliorative process label learner 144 may be bestsuited for a different particular machine-learning model.

Continuing to refer to FIG. 1 , in an embodiment at least a server 104may be configured, for instance as part of selecting second training set152, to associate the at least second prognostic label 156 with at leasta category from a list of significant categories of prognostic labels.This may be performed as described above for use of lists of significantcategories with regard to at least a first prognostic label.Significance may be determined, and/or association with at least acategory, may be performed for prognostic labels in first training set120 according to a first process as described above and for prognosticlabels in second training set 152 according to a second process asdescribed above.

Still referring to FIG. 1 , at least a server 104 may be configured, forinstance as part of selecting second training set 152, to associate atleast a correlated ameliorative process label 160 with at least acategory from a list of significant categories of ameliorative processlabels 120. In an embodiment, at least a server 104 and/or a user deviceconnected to at least a server 104 may provide a second graphical userinterface 164 which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of prognostic labelsthat the experts consider to be significant as described above; fieldsin graphical user interface may provide options describing previouslyidentified categories, which may include a comprehensive ornear-comprehensive list of types of prognostic labels, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface or the like may include fieldscorresponding to ameliorative labels, where experts may enter datadescribing ameliorative labels and/or categories of ameliorative labelsthe experts consider related to entered categories of prognostic labels;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded ameliorativelabels, and which may be comprehensive, permitting each expert to selectan ameliorative label and/or a plurality of ameliorative labels theexpert believes to be predicted and/or associated with each category ofprognostic labels selected by the expert. Fields for entry ofameliorative labels and/or categories of ameliorative labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of ameliorative labels may enable anexpert to select and/or enter information describing or linked to acategory of ameliorative label that the expert considers significant,where significance may indicate likely impact on longevity, mortality,quality of life, or the like as described in further detail below.Graphical user interface may provide an expert with a field in which toindicate a reference to a document describing significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or significant categories of ameliorative labels. Suchinformation may alternatively be entered according to any other suitablemeans for entry of expert data as described above. Data concerningsignificant categories of prognostic labels, relationships of suchcategories to ameliorative labels, and/or significant categories ofameliorative labels may be entered using analysis of documents usinglanguage processing module 136 or the like as described above.

In an embodiment, and still referring to FIG. 1 , at least a server 104may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. At least a server 104 may be configured, for instanceas part of receiving second training set 152, to receive at least adocument describing at least a medical history and extract at least asecond data entry of plurality of second data entries from the at leasta document. A medical history document may include, for instance, adocument received from an expert and/or medical practitioner describingtreatment of a patient; document may be anonymized by removal of one ormore patient-identifying features from document. A medical historydocument may include a case study, such as a case study published in amedical journal or written up by an expert. A medical history documentmay contain data describing and/or described by a prognostic label; forinstance, the medical history document may list a diagnosis that amedical practitioner made concerning the patient, a finding that thepatient is at risk for a given condition and/or evinces some precursorstate for the condition, or the like. A medical history document maycontain data describing and/or described by an ameliorative processlabel; for instance, the medical history document may list a therapy,recommendation, or other ameliorative process that a medicalpractitioner described or recommended to a patient. A medical historydocument may describe an outcome; for instance, medical history documentmay describe an improvement in a condition describing or described by aprognostic label, and/or may describe that the condition did notimprove. Prognostic labels, ameliorative process labels 120, and/orefficacy of ameliorative process labels 120 may be extracted from and/ordetermined from one or more medical history documents using anyprocesses for language processing as described above; for instance,language processing module 136 may perform such processes. As anon-limiting example, positive and/or negative indications regardingameliorative processes identified in medical history documents may bedetermined in a manner described above for determination of positiveand/or negative indications regarding categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels.

With continued reference to FIG. 1 , at least a server 104 may beconfigured, for instance as part of receiving second training set 152,to receive at least a second data entry of the plurality of second dataentries from at least an expert. This may be performed, withoutlimitation using second graphical user interface 164 as described above.

With continued reference to FIG. 1 , at least a server 104 is configuredto select training data including at least a first training set 120 andat least a second training set 152. At least a server 104 may beconfigured to select at least a first training set 120 as a function ofat least a biological extraction classifier label. At least a server 104may be configured to select at least a first training set 120 as afunction of at least a physically extracted sample contained within atleast a biological extraction. For example, at least a biologicalextraction containing a stool sample may be utilized to select at leasta first training set 120 that includes physiological state data 124 thatincludes a stool sample. In yet another non-limiting example, at least abiological extraction containing a urine sample may be utilized toselect at least a first training set 120 that includes physiologicalstate data 124 that includes a urine sample. Training data may beorganized according to physiological categories and contained within atraining set database as described in more detail below.

With continued reference to FIG. 1 , at least a server 104 may beconfigured to generate a prognostic output and/or ameliorative outputbased on the at least a biological extraction, the prognostic outputand/or ameliorative output including at least a prognostic output and atleast an ameliorative output using the at least a first training set,the at least a second training set, and the at least a biologicalextraction.

With continued reference to FIG. 1 , system 100 includes a descriptorgenerator module 168 operating on the at least a server wherein thedescriptor generator module 168 is designed and configured to generateat least a descriptor trail 172 wherein the at least a descriptor trail172 includes at least an element of diagnostic data 176. Descriptorgenerator module 168 may include any hardware and/or software module. Adescriptor trail 172, as used herein, includes a datum containing atleast an element of diagnostic data and at least a generator descriptorcontaining data describing processes utilized and selected to generateand/or calculate a prognostic output and/or ameliorative output.Descriptor trail 172 includes at least an element of diagnostic data176. Diagnostic data 176, as used herein, includes any data and/or dataelement used to generate a prognostic output and/or ameliorative output.Diagnostic data 176 may include at least a biological extraction,biological extraction classifier label, training data, a first trainingset, a second training set, a prognostic output, an ameliorative output,a prognostic output and/or ameliorative output, any machine-learningmodel utilized to generate a prognostic output and/or ameliorativeoutput, a plurality of prognostic outputs, a plurality of ameliorativeoutputs, any regression models, weighted variables, confidence levels,error functions, datasets, models, and/or calculations utilized togenerate a prognostic output and/or ameliorative output. For example,diagnostic data 176 may include a plurality of prognostic outputs andfurther calculations utilized to select a particular prognostic outputfrom the plurality of prognostic outputs to be included in theprognostic output and/or ameliorative output. In yet anothernon-limiting example, diagnostic data 176 may include a first trainingset 120 that is utilized to generate a prognostic output, or abiological extraction utilized to select at least a first training set.In an embodiment, diagnostic data 176 may include an element ofdiagnostic data 176 such as a segment of a second training set 152 whichdoes not include the entire second training set. In an embodiment,diagnostic data 176 may include a plurality of training sets utilized togenerate an ameliorative output or a confidence interval associated witha particular prognostic output. In an embodiment, diagnostic data 176may include a supervised machine-learning algorithm utilized to generatea prognostic output. In an embodiment, diagnostic data 176 may include alazy-learning process utilized to generate an ameliorative output.Descriptor trail 172 includes generator descriptor 180. Generatordescriptor 180, as used herein, includes any datum describing generationand/or calculation of a prognostic output and/or ameliorative output,and/or any process therefor. For example, generator descriptor 180 mayinclude a description of a particular supervised machine-learning modelselected to generate a prognostic output including for example adescription of particular training sets utilized to generate thesupervised machine-learning model in addition to particular trainingsets that were not selected and not utilized to generate the supervisedmachine-learning model. In yet another non-limiting example, generatordescriptor 180 may include a description of a particular hierarchicalclustering model utilized to generate an ameliorative output that mayinclude a description of certain data clusters generated and utilized inthe hierarchical clustering model as well as a description of which dataclusters were discarded and not utilized. Generator descriptor 180 doesnot contain diagnostic data 176.

With continued reference to FIG. 1 , descriptor generator module 168 maybe configured to receive at least an advisor filter input containing atleast a diagnostic data 176 selection, generate at least a descriptortrail 172 as a function of the at least an advisor filter, and transmitthe at least a descriptor trail 172 to at least an advisor clientdevice. An advisor filter input, as used herein is an input datumreceived from at least an informed advisor containing a specificationfor a particular element and/or element of diagnostic data 176. Informedadvisor is defined for the purposes of this disclosure as any personbesides the user who has access to information useable to aid user ininteraction with artificial intelligence advisory system. An informedadvisor may include, without limitation, a medical professional such asa doctor, nurse, nurse practitioner, functional medicine practitioner,any professional with a career in medicine, nutrition, genetics,fitness, life sciences, insurance, and/or any other applicable industrythat may contribute information and data to system 100 regarding medicalneeds. An informed advisor may include a spiritual or philosophicaladvisor, such as a religious leader, pastor, imam, rabbi, or the like.An informed advisor may include a physical fitness advisor, such aswithout limitation a personal trainer, instructor in yoga or martialarts, sports coach, or the like. Informed advisor may generate anadvisor filter input at an advisor client device as described in moredetail below. For instance and without limitation, advisor filter inputmay include a request for a particular machine-learning model utilizedto generate a prognostic output. In yet another non-limiting example,advisor filter input may include a request for a plurality of prognosticoutputs generated by prognostic label leaner and calculated confidencelevels utilized to select a prognostic output from the plurality ofprognostic outputs utilized to generate a prognostic output and/orameliorative output. In yet another non-limiting example, advisor filterinput may include a request for all machine-learning algorithms utilizedto generate prognostic output and/or ameliorative output. In anembodiment, advisor filter input may not include any preference ofdiagnostic data 176 whereby all diagnostic data 176 may be transmittedto at least an informed advisor which may include for example andillustrative purposes only a biological extraction, training data, firsttraining set, second trainings set, prognostic output and/orameliorative output, prognostic output, ameliorative output, prognosticmachine-learning model, ameliorative machine-learning model, regressionmodels, lazy-learning models, confidence intervals, biologicalextraction classifier label, and the like.

With continued reference to FIG. 1 , system 100 may include at least anadvisor client device. Advisor client device 184 may include, withoutlimitation, a display in communication with at least a server, displaymay include any display as described here. Advisor client device 184 mayinclude an additional computing device, such as a mobile device, laptop,desktop computer, or the like; as a non-limiting example, advisor clientdevice 184 may be a computer and/or workstation operated by a medicalprofessional. Output may be displayed on at least an advisor clientdevice 184 using an output graphical user interface.

With continued reference to FIG. 1 , system 100 may include at least auser client device 188. User client device 188 may include any devicesuitable for use as an advisor client device 184 as described above.User client device 188 may operate on system 100 and may receive anoutput such as prognostic output and/or ameliorative output. In anembodiment, user client device 188 may communicate with advisor clientdevice 184 and/or system 100 over a network, including any of thenetworks as described herein.

Referring now to FIG. 2 , data incorporated in first training set 120and/or second training set 152 may be incorporated in one or moredatabases. As a non-limiting example, one or elements of physiologicalstate data 124 may be stored in and/or retrieved from a biologicalextraction database 200. A biological extraction database 200 mayinclude any data structure for ordered storage and retrieval of data,which may be implemented as a hardware or software module. A biologicalextraction database 200 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. A biological extraction database 200 mayinclude a plurality of data entries and/or records corresponding toelements of physiological data as described above. Data entries and/orrecords may describe, without limitation, data concerning particularphysiological samples that have been collected; entries may describereasons for collection of samples, such as without limitation one ormore conditions being tested for, which may be listed with relatedprognostic labels. Data entries may include prognostic labels and/orother descriptive entries describing results of evaluation of pastphysiological samples, including diagnoses that were associated withsuch samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by system 100 in previous iterations of methods,with or without validation of correctness by medical professionals. Dataentries in a biological extraction database 200 may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database; one or moreadditional elements of information may include data associating aphysiological sample and/or a person from whom a physiological samplewas extracted or received with one or more cohorts, includingdemographic groupings such as ethnicity, sex, age, income, geographicalregion, or the like, one or more common diagnoses or physiologicalattributes shared with other persons having physiological samplesreflected in other data entries, or the like. Additional elements ofinformation may include one or more categories of physiological data asdescribed above. Additional elements of information may includedescriptions of particular methods used to obtain physiological samples,such as without limitation physical extraction of blood samples or thelike, capture of data with one or more sensor 108, and/or any otherinformation concerning provenance and/or history of data acquisition.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in abiological extraction database 200 may reflect categories, cohorts,and/or populations of data consistently with this disclosure.

With continued reference to FIG. 2 , at least a server 104 and/oranother device in system 100 may populate one or more fields inbiological extraction database 200 using expert information, which maybe extracted or retrieved from an expert knowledge database 204. Anexpert knowledge database 204 may include any data structure and/or datastore suitable for use as a biological extraction database 200 asdescribed below. Expert knowledge database 204 may include data entriesreflecting one or more expert submissions of data such as may have beensubmitted according to any process described above in reference to FIG.1 , including without limitation by using first graphical user interface132 and/or second graphical user interface 164. Expert knowledgedatabase may include one or more fields generated by language processingmodule 136, such as without limitation fields extracted from one or moredocuments as described above. For instance, and without limitation, oneor more categories of physiological data and/or related prognosticlabels and/or categories of prognostic labels associated with an elementof physiological state data 124 as described above may be stored ingeneralized from in an expert knowledge database 204 and linked to,entered in, or associated with entries in a biological extractiondatabase 200. Documents may be stored and/or retrieved by at least aserver 104 and/or language processing module 136 in and/or from adocument database 208; document database 208 may include any datastructure and/or data store suitable for use as biological extractiondatabase 200 as described above. Documents in document database 208 maybe linked to and/or retrieved using document identifiers such as URIand/or URL data, citation data, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which documents may be indexed and retrieved according tocitation, subject matter, author, date, or the like as consistent withthis disclosure.

With continued reference to FIG. 2 , a prognostic label database 212,which may be implemented in any manner suitable for implementation ofbiological extraction database 200, may be used to store prognosticlabels used in system 100, including any prognostic labels correlatedwith elements of physiological data in first training set 120 asdescribed above; prognostic labels may be linked to or refer to entriesin biological extraction database 200 to which prognostic labelscorrespond. Linking may be performed by reference to historical dataconcerning physiological samples, such as diagnoses, prognoses, and/orother medical conclusions derived from physiological samples in thepast; alternatively or additionally, a relationship between a prognosticlabel and a data entry in biological extraction database 200 may bedetermined by reference to a record in an expert knowledge database 204linking a given prognostic label to a given category of physiologicalsample as described above. Entries in prognostic label database 212 maybe associated with one or more categories of prognostic labels asdescribed above, for instance using data stored in and/or extracted froman expert knowledge database 204.

With continued reference to FIG. 2 , first training set 120 may bepopulated by retrieval of one or more records from biological extractiondatabase 200 and/or prognostic label database 212; in an embodiment,entries retrieved from biological extraction database 200 and/orprognostic label database 212 may be filtered and or select via query tomatch one or more additional elements of information as described above,so as to retrieve a first training set 120 including data belonging to agiven cohort, demographic population, or other set, so as to generateoutputs as described below that are tailored to a person or persons withregard to whom system 100 classifies physiological samples to prognosticlabels as set forth in further detail below. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which records may be retrieved from biological extractiondatabase 200 and/or prognostic label database to generate a firsttraining set 120 to reflect individualized group data pertaining to aperson of interest in operation of system and/or method, includingwithout limitation a person with regard to whom at least a physiologicalsample is being evaluated as described in further detail below. At leasta server 104 may alternatively or additionally receive a first trainingset 120 and store one or more entries in biological extraction database200 and/or prognostic label database 212 as extracted from elements offirst training set.

With continued reference to FIG. 2 , system 100 may include orcommunicate with an ameliorative process label database 216; anameliorative process label database 216 may include any data structureand/or datastore suitable for use as a biological extraction database200 as described above. An ameliorative process label database 216 mayinclude one or more entries listing labels associated with one or moreameliorative processes as described above, including any ameliorativelabels correlated with prognostic labels in second training set 152 asdescribed above; ameliorative process labels may be linked to or referto entries in prognostic label database 212 to which ameliorativeprocess labels correspond. Linking may be performed by reference tohistorical data concerning prognostic labels, such as therapies,treatments, and/or lifestyle or dietary choices chosen to alleviateconditions associated with prognostic labels in the past; alternativelyor additionally, a relationship between an ameliorative process label160 and a data entry in prognostic label database 212 may be determinedby reference to a record in an expert knowledge database 204 linking agiven ameliorative process label 160 to a given category of prognosticlabel as described above. Entries in ameliorative process label database216 may be associated with one or more categories of prognostic labelsas described above, for instance using data stored in and/or extractedfrom an expert knowledge database 204.

With continued reference to FIG. 2 , second training set 152 may bepopulated by retrieval of one or more records from prognostic labeldatabase 212 and/or ameliorative process label database 216; in anembodiment, entries retrieved from prognostic label database 212 and/orameliorative process label database 216 may be filtered and or selectvia query to match one or more additional elements of information asdescribed above, so as to retrieve a second training set 152 includingdata belonging to a given cohort, demographic population, or other set,so as to generate outputs as described below that are tailored to aperson or persons with regard to whom system 100 classifies prognosticlabels to ameliorative process labels as set forth in further detailbelow. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which records may beretrieved from prognostic label database 212 and/or ameliorative processlabel database 216 to generate a second training set 152 to reflectindividualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a physiological sample is being evaluatedas described in further detail below. At least a server 104 mayalternatively or additionally receive a second training set 152 andstore one or more entries in prognostic label database 212 and/orameliorative process label database 216 as extracted from elements ofsecond training set.

With continued reference to FIG. 2 , at least a server 104 may receivean update to one or more elements of data represented in first trainingset 120 and/or second training set, and may perform one or moremodifications to first training set 120 and/or second training set, orto biological extraction database 200, expert knowledge database 204,prognostic label database 212, and/or ameliorative process labeldatabase 216 as a result. For instance, a physiological sample may turnout to have been erroneously recorded; at least a server 104 may removeit from first training set, second training set, biological extractiondatabase 200, expert knowledge database 204, prognostic label database212, and/or ameliorative process label database 216 as a result. As afurther example, a medical and/or academic paper, or a study on which itwas based, may be revoked; at least a server 104 may remove it fromfirst training set, second training set, biological extraction database200, expert knowledge database 204, prognostic label database 212,and/or ameliorative process label database 216 as a result. Informationprovided by an expert may likewise be removed if the expert losescredentials or is revealed to have acted fraudulently.

Continuing to refer to FIG. 2 , elements of data first training set,second training set, biological extraction database 200, expertknowledge database 204, prognostic label database 212, and/orameliorative process label database 216 may have temporal attributes,such as timestamps; at least a server 104 may order such elementsaccording to recency, select only elements more recently entered forfirst training set 120 and/or second training set, or otherwise biastraining sets, database entries, and/or machine-learning models asdescribed in further detail below toward more recent or less recententries. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which temporal attributesof data entries may be used to affect results of methods and/or systemsas described herein.

Referring now to FIG. 3 , an exemplary embedment of biologicalextraction database 200 is illustrated, which may be implemented,without limitation, as a hardware or software module. Biologicalextraction database 200 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. One or more database tables in biologicalextraction database 200 may include, as a non-limiting example, aprognostic link table 300. Prognostic link table 300 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface132 as described above, one or more rows recording such an entry may beinserted in prognostic link table 300. Alternatively or additionally,linking of prognostic labels to physiological sample data may beperformed entirely in a prognostic label database as described below.

With continued reference to FIG. 3 , biological extraction database 200may include tables listing one or more samples according to samplesource. For instance, and without limitation, biological extractiondatabase 200 may include a fluid sample table 304 listing samplesacquired from a person by extraction of fluids, such as withoutlimitation blood, lymph cerebrospinal fluid, or the like. As anothernon-limiting example, biological extraction database 200 may include asensor 108 data table 308, which may list samples acquired using one ormore sensor 108, for instance as described in further detail below. As afurther non-limiting example, biological extraction database 200 mayinclude a genetic sample table 312, which may list partial or entiresequences of genetic material. Genetic material may be extracted andamplified, as a non-limiting example, using polymerase chain reactions(PCR) or the like. As a further example, also non-limiting, biologicalextraction database 200 may include a medical report table 316, whichmay list textual descriptions of medical tests, including withoutlimitation radiological tests or tests of strength and/or dexterity orthe like. Data in medical report table may be sorted and/or categorizedusing a language processing module 136, for instance, translating atextual description into a numerical value and a label corresponding toa category of physiological data; this may be performed using anylanguage processing algorithm or algorithms as referred to in thisdisclosure. As another non-limiting example, biological extractiondatabase 200 may include a tissue sample table 320, which may recordphysiological samples obtained using tissue samples. Tables presentedabove are presented for exemplary purposes only; persons skilled in theart will be aware of various ways in which data may be organized inbiological extraction database 200 consistently with this disclosure.

Referring now to FIG. 4 , an exemplary embodiment of an expert knowledgedatabase 204 is illustrated. Expert knowledge database 204 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 204 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 200 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 4 , one or more database tables in expertknowledge database 204 may include, as a non-limiting example, an expertprognostic table 400. Expert prognostic table 400 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface132 as described above, one or more rows recording such an entry may beinserted in expert prognostic table 400. In an embodiment, a formsprocessing module 404 may sort data entered in a submission via firstgraphical user interface 132 by, for instance, sorting data from entriesin the first graphical user interface 132 to related categories of data;for instance, data entered in an entry relating in the first graphicaluser interface 132 to a prognostic label may be sorted into variablesand/or data structures for storage of prognostic labels, while dataentered in an entry relating to a category of physiological data and/oran element thereof may be sorted into variables and/or data structuresfor the storage of, respectively, categories of physiological data orelements of physiological data. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, language processingmodule 136 may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map physiological data to an existing label.Alternatively or additionally, when a language processing algorithm,such as vector similarity comparison, indicates that an entry is not asynonym of an existing label, language processing module 136 mayindicate that entry should be treated as relating to a new label; thismay be determined by, e.g., comparison to a threshold number of cosinesimilarity and/or other geometric measures of vector similarity of theentered text to a nearest existent label, and determination that adegree of similarity falls below the threshold number and/or a degree ofdissimilarity falls above the threshold number. Data from expert textualsubmissions 408, such as accomplished by filling out a paper or PDF formand/or submitting narrative information, may likewise be processed usinglanguage processing module 136. Data may be extracted from expert papers412, which may include without limitation publications in medical and/orscientific journals, by language processing module 136 via any suitableprocess as described herein. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalmethods whereby novel terms may be separated from already-classifiedterms and/or synonyms therefore, as consistent with this disclosure.Expert prognostic table 400 may include a single table and/or aplurality of tables; plurality of tables may include tables forparticular categories of prognostic labels such as a current diagnosistable, a future prognosis table, a genetic tendency table, a metabolictendency table, and/or an endocrinal tendency table (not shown), to namea few non-limiting examples presented for illustrative purposes only.

With continued reference to FIG. 4 , one or more database tables inexpert knowledge database 204 may include, as a further non-limitingexample tables listing one or more ameliorative process labels; expertdata populating such tables may be provided, without limitation, usingany process described above, including entry of data from secondgraphical user interface 164 via forms processing module 404 and/orlanguage processing module 136, processing of textual submissions 408,or processing of expert papers 412. For instance, and withoutlimitation, an ameliorative nutrition table 416 may list one or moreameliorative processes based on nutritional instructions, and/or linksof such one or more ameliorative processes to prognostic labels, asprovided by experts according to any method of processing and/orentering expert data as described above. As a further example anameliorative action table 420 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above. As an additional example, an ameliorativesupplement table 424 may list one or more ameliorative processes basedon nutritional supplements, such as vitamin pills or the like, and/orlinks of such one or more ameliorative processes to prognostic labels,as provided by experts according to any method of processing and/orentering expert data as described above. As a further non-limitingexample, an ameliorative medication table 428 may list one or moreameliorative processes based on medications, including withoutlimitation over-the-counter and prescription pharmaceutical drugs,and/or links of such one or more ameliorative processes to prognosticlabels, as provided by experts according to any method of processingand/or entering expert data as described above. As an additionalexample, a counterindication table 432 may list one or morecounter-indications for one or more ameliorative processes;counterindications may include, without limitation allergies to one ormore foods, medications, and/or supplements, side-effects of one or moremedications and/or supplements, interactions between medications, foods,and/or supplements, exercises that should not be used given one or moremedical conditions, injuries, disabilities, and/or demographiccategories, or the like. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database204 consistently with this disclosure.

Referring now to FIG. 5 , an exemplary embodiment of a prognostic labeldatabase 212 is illustrated. Prognostic label database 212 may, as anon-limiting example, organize data stored in the prognostic labeldatabase 212 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofprognostic label database 212 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 5 , one or more database tables in prognosticlabel database 212 may include, as a non-limiting example, a sample datatable 500. Sample data table 500 may be a table listing sample data,along with, for instance, one or more linking columns to link such datato other information stored in prognostic label database 212. In anembodiment, sample data 504 may be acquired, for instance frombiological extraction database 200, in a raw or unsorted form, and maybe translated into standard forms, such as standard units ofmeasurement, labels associated with particular physiological datavalues, or the like; this may be accomplished using a datastandardization module 508, which may perform unit conversions. Datastandardization module 508 may alternatively or additionally map textualinformation, such as labels describing values tested for or the like,using language processing module 136 or equivalent components and/oralgorithms thereto.

Continuing to refer to FIG. 5 , prognostic label database 212 mayinclude a sample label table 512; sample label table 512 may listprognostic labels received with and/or extracted from physiologicalsamples, for instance as received in the form of sample text 516. Alanguage processing module 136 may compare textual information soreceived to prognostic labels and/or form new prognostic labelsaccording to any suitable process as described above. Sample prognosticlink table may combine samples with prognostic labels, as acquired fromsample label table and/or expert knowledge database 204; combination maybe performed by listing together in rows or by relating indices orcommon columns of two or more tables to each other. Tables presentedabove are presented for exemplary purposes only; persons skilled in theart will be aware of various ways in which data may be organized inexpert knowledge database 204 consistently with this disclosure.

Referring now to FIG. 6 , an exemplary embodiment of an ameliorativeprocess label database 216 is illustrated. Ameliorative process labeldatabase 216 may, as a non-limiting example, organize data stored in theameliorative process label database 216 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of ameliorative process label database 216 mayinclude an identifier of an expert submission, such as a form entry,textual submission, expert paper, or the like, for instance as definedbelow; as a result, a query may be able to retrieve all rows from anytable pertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 6 , ameliorative process label database 216 mayinclude a prognostic link table 600; prognostic link table may linkameliorative process data to prognostic label data, using any suitablemethod for linking data in two or more tables as described above.Ameliorative process label database 216 may include an ameliorativenutrition table 604, which may list one or more ameliorative processesbased on nutritional instructions, and/or links of such one or moreameliorative processes to prognostic labels, for instance as provided byexperts according to any method of processing and/or entering expertdata as described above, and/or using one or more machine-learningprocesses as set forth in further detail below. As a further example anameliorative action table 608 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above and/or using one or more machine-learningprocesses as set forth in further detail below. As an additionalexample, an ameliorative supplement table 612 may list one or moreameliorative processes based on nutritional supplements, such as vitaminpills or the like, and/or links of such one or more ameliorativeprocesses to prognostic labels, as provided by experts according to anymethod of processing and/or entering expert data as described aboveand/or using one or more machine-learning processes as set forth infurther detail below. As a further non-limiting example, an ameliorativemedication table 616 may list one or more ameliorative processes basedon medications, including without limitation over-the-counter andprescription pharmaceutical drugs, and/or links of such one or moreameliorative processes to prognostic labels, as provided by expertsaccording to any method of processing and/or entering expert data asdescribed above and/or using one or more machine-learning processes asset forth in further detail below. As an additional example, acounterindication table 620 may list one or more counter-indications forone or more ameliorative processes; counterindications may include,without limitation allergies to one or more foods, medications, and/orsupplements, side-effects of one or more medications and/or supplements,interactions between medications, foods, and/or supplements, exercisesthat should not be used given one or more medical conditions, injuries,disabilities, and/or demographic categories, or the like; this may beacquired using expert submission as described above and/or using one ormore machine-learning processes as set forth in further detail below.Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in ameliorative process database 216 consistently withthis disclosure.

Referring now to FIG. 7 , an exemplary embodiment of biologicalextraction classifier label database 700 is illustrated. Biologicalextraction classifier label database 700 may, as a non-limiting example,organize data stored in the biological extraction classifier labeldatabase 700 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofbiological extraction classifier label database 700 may include anidentifier of an expert submission, such as a form entry, textualsubmission, expert paper, or the like, for instance as defined below; asa result, a query may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

With continued reference to FIG. 7 , biological extraction classifierlabel database 700 may include any data structure for ordered storageand retrieval of data, which may be implemented as a hardware orsoftware module, and which may be implemented as any database structuresuitable for use as biological extraction database 200. Biologicalextraction classifier label database 700 may include normal label table704; normal label table 704 may list one or more biological extractionscontaining a “normal” classifier label indicating that a givenbiological extraction has fallen within a given reference range. Forexample, normal label table 704 may include a blood hemoglobin level fora female of 14.0 grams per deciliter (g/dL) containing a normalclassifier label as compared to a reference range of 13.5 to 17.5 gramsper deciliter (g/dL). Biological extraction classifier label database700 may include elevated label table 708; elevated label table 708 maylist one or more biological extractions containing an “elevated”classifier label indicating a given biological extraction has fallenabove a given reference range. For example, elevated label table 708 mayinclude a urine sample containing a protein level of 37 milligrams perdeciliter (mg/dL) as compared to a reference range of 0 to 20 milligramsper deciliter (mg/dL). Biological extraction classifier label database700 may include low label table 712; low label table 712 may list one ormore biological extractions containing a “low” classifier labelindicating a given biological extraction has fallen below a givenreference range. For example, low label table 712 may include a salivarysample containing a progesterone level of 55 nanograms per milliliter(ng/ml) as compared to a reference range of 75-250 nanograms permilliliter (ng/ml). Biological extraction classifier label database 700may include abnormal findings label table 716; abnormal findings labeltable 716 may list one or more biological extractions containing anabnormal findings label indicating a given biological extraction has notfallen within a given reference range. For example, abnormal findingstable 716 may include a hemoglobin A1c level OF 8% as compared to areference range of 4-5.6%. Biological extraction classifier labeldatabase 700 may include MD alert label table 720; MD alert label table720 may include one or more biological extractions containing an MDalert label indicating that a medical doctor needs to be consulted,which may be due to abnormally high or low levels, dangerous conditionsthat may be indicated as a result of a particular level, and/or an alertthat time is of the essence and medical attention is warrantedimmediately. For example, MD alert label table 720 may include a bloodserum pH of 6.5 as compared to a reference range of 7.35 to 7.45 whichmay indicate possible diabetic ketoacidosis and warrant medicalattention immediately. Biological extraction classifier label database700 may include reference range table 724; reference range table 724 maylist one or more reference ranges for one or more biologicalextractions. Reference ranges may be organized and categorized byvarious categories and factors such as reference ranges for particularages of individuals, gender, co-morbid conditions, menstrual phase,menstrual state, and the like.

Referring now to FIG. 8 , an exemplary embodiment of physiologicalcategories database 800 is illustrated. Physiological categoriesdatabase 800 may, as a non-limiting example, organize data stored in thephysiological categories database 800 according to one or more databasetables. One or more database tables may be linked to one another by, forinstance, common column values. For instance, a common column betweentwo tables of physiological categories database 800 may include anidentifier of an expert submission, such as a form entry, textualsubmission, expert paper, or the like, for instance as defined below; asa result, a query may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

With continued reference to FIG. 8 , physiological categories database800 include any data structure for ordered storage and retrieval ofdata, which may be implemented as a hardware or software module, andwhich may be implemented as any database structure suitable for use asbiological extraction database 200. Physiological categories database800 may include cardiovascular table 804; cardiovascular table 804 mayinclude physiological state data 124 and/or biological extractionscategorized as relating to cardiovascular system. For example,cardiovascular table 804 may include data relating to heart health suchas lipid levels, troponin levels, electrocardiograms, triglycerides,myoglobin, creatine kinase, and the like. Physiological categoriesdatabase 800 may include digestive table 808; digestive table 808 mayinclude physiological state data 124 and/or biological extractionscategorized as relating to digestive system. For example, digestivetable 808 may include data relating to digestive health such as ironlevels, ferritin levels, microbiome species within gut, Candida albicansspecies, calprotectin levels, lactoferrin levels, neopterin levels,lysozyme levels, and the like. Physiological categories database 800 mayinclude dermatology table 812; dermatology table 812 may includephysiological state data 124 and/or biological extractions categorizedas relating to dermatology. For example, dermatology table 812 mayinclude skin biopsies, mole biopsies, immunoglobulin a (IGA) levels,cutaneous electron micrographs, immunohistochemical hybridizationstains, histochemical hybridization stains, and in situ hybridizationstains. Physiological categories database 800 may include functionalmedicine table 816; functional medicine table 816 may includephysiological state data 124 and/or biological extractions categorizedas relating to functional medicine. For example, functional medicinetable 816 may include neurotransmitter levels, hormone levels, basalbody temperature, total T4 levels, free T4 levels, free T3 levels, gutmetabolic markers, digestion absorption markers, immune systemhyperactivity, eosinophil protein x levels, calprotectin levels, and thelike. Physiological categories database 800 may include renal table 820;renal table 820 may include physiological state data 124 and/orbiological extractions categorized as relating to the renal system. Forexample, renal table 820 may include albumin levels, creatinine levels,glomerular filtration rate, creatinine clearance, blood urea nitrogen,urinalysis, urine protein, microalbuminuria and the like. Physiologicalcategories database 800 may include endocrine table 824; endocrine table824 may include physiological state data 124 and/or biologicalextractions categorized as relating to the endocrine system. Forexample, endocrine table 824 may include aldosterone levels, serumcortisol levels, hirsutism panel, adiponectin, fasting glucose level,insulin levels, hemoglobin A1C with calculated mean plasma glucose(MPG), prolactin, pituitary hormone level, and the like. In anembodiment, physiological state data 124 and/or biological extractionsmay be categorized to one or more tables contained within physiologicalcategories database 800. Other tables not illustrated but which may beincluded in physiological categories database 800 may include forexample, circulatory table, excretory table, immune table, lymphatictable, muscular table, neural table, urinary table, respiratory table,reproductive table, and the like.

Referring now to FIG. 9 , an exemplary embodiment of training setdatabase 900 is illustrated. Training set database 900 may, as anon-limiting example, organize data stored in the training set database900 according to one or more database tables. One or more databasetables may be linked to one another by, for instance, common columnvalues. For instance, a common column between two tables of training setdatabase 900 may include an identifier of an expert submission, such asa form entry, textual submission, expert paper, or the like, forinstance as defined below; as a result, a query may be able to retrieveall rows from any table pertaining to a given submission or set thereof.Other columns may include any other category usable for organization orsubdivision of expert data, including types of expert data, names and/oridentifiers of experts submitting the data, times of submission, or thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which expert data from oneor more tables may be linked and/or related to expert data in one ormore other tables.

With continued reference to FIG. 9 , training set database 900 mayinclude any data structure for ordered storage and retrieval of data,which may be implemented as a hardware or software module, and which maybe implemented as any database structure suitable for use as biologicalextraction database 200. Training set database 900 may include one ormore tables containing training sets categorized by physiologicalcategory and sample type. Training set database 900 may includecardiovascular blood sample table 904; cardiovascular blood sample table904 may include training sets containing physiological state data 124categorized as relating to cardiovascular system and which includephysiological data extracted from a blood sample. For example, bloodsample table 904 may include training sets containing physiologicalstate data 124 such as total cholesterol levels or total triglyceridelevels. Training set database 900 may include digestive stool sampletable 908; digestive stool sample table 908 may include training setscontaining physiological state data 124 categorized as relating todigestive system and which include physiological data extracted from astool sample. For example, digestive stool sample table 908 may includetraining sets containing physiological state data 124 such as an ova andparasite exam. Training set database 900 may include dermatology skinbiopsy table 912; dermatology skin biopsy table 912 may include trainingsets containing physiological state data 124 categorized as relating todermatology system and which include physiological data extracted from askin biopsy. For example, skin biopsy table 912 may include trainingsets containing physiological state data 124 such as a skin sampleremoved from an area on the body such as the elbow or knee and the like.Training set database 900 may include neurological cerebrospinal fluidtable 916; neurological cerebrospinal fluid table 916 may includetraining sets containing physiological state data 124 categorized asrelating to neurology system and which include physiological dataextracted from cerebrospinal fluid. For example, neurologicalcerebrospinal fluid table 916 may include for example pressuremeasurement, cell count, white cell differential, glucose levels,protein levels, gram stain, culture, and the like. Training set database900 may include endocrine blood hormone table 920; endocrine bloodhormone table 920 may include training sets containing physiologicalstate data 124 categorized as relating to endocrine system and whichinclude physiological data extracted from blood sample. For example,endocrine blood hormone table 920 may include for example estradiollevels, progesterone levels, follicle stimulating hormone, testosterone,thyroid stimulating hormone (TSH), thyroxine (T4), and the like.Training set database 900 may include physiological categories linktable 924; physiological categorizes link table 924 may relatephysiological state data 124 and/or biological extractions tophysiological categories. For example, physiological categories linktable 924 may include entries from an expert relating physiologicalstate data 124 to a physiological category. In an embodiment, trainingsets contained within training set database and contained within one ormore tables may include at least a data entry of a first training set120 that includes at least an element of physiological state data 124and at least a correlated first prognostic label. In an embodiment,first training set 120 may be correlated to a second training set 152that includes at least a second data entry of a second training set 152where each second data entry of the second training set 152 includes atleast a second prognostic label 156 and at least a correlatedameliorative process label. In an embodiment, training sets containedwithin training set database 900 and/or data entries contained with eachtraining set may be listed and contained within more than one tablewithin training set database 900. In an embodiment, training setdatabase 900 may include other tables not illustrated including forexample cardiovascular saliva sample table, digestive blood sampletable, dermatology blood sample table, neurological saliva sample,neurological amniotic sample, neurological hair sample, endocrine hairsample, and the like.

Referring now to FIG. 10 , an exemplary embodiment of prognostic labellearner 112 is illustrated. Machine-learning algorithms used byprognostic label learner 112 may include supervised machine-learningalgorithms, which may, as a non-limiting example be executed using asupervised learning module 1000 executing on at least a server 104and/or on another computing device in communication with at least aserver 104, which may include any hardware or software module.Supervised machine-learning algorithms, as defined herein, includealgorithms that receive a training set relating a number of inputs to anumber of outputs, and seek to find one or more mathematical relationsrelating inputs to outputs, where each of the one or more mathematicalrelations is optimal according to some criterion specified to thealgorithm using some scoring function. For instance, a supervisedlearning algorithm may use elements of physiological data as inputs,prognostic labels as outputs, and a scoring function representing adesired form of relationship to be detected between elements ofphysiological data and prognostic labels; scoring function may, forinstance, seek to maximize the probability that a given element ofphysiological state data 124 and/or combination of elements ofphysiological data is associated with a given prognostic label and/orcombination of prognostic labels to minimize the probability that agiven element of physiological state data 124 and/or combination ofelements of physiological state data 124 is not associated with a givenprognostic label and/or combination of prognostic labels. Scoringfunction may be expressed as a risk function representing an “expectedloss” of an algorithm relating inputs to outputs, where loss is computedas an error function representing a degree to which a predictiongenerated by the relation is incorrect when compared to a giveninput-output pair provided in first training set. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious possible variations of supervised machine-learning algorithmsthat may be used to determine relation between elements of physiologicaldata and prognostic labels. In an embodiment, one or more supervisedmachine-learning algorithms may be restricted to a particular domain forinstance, a supervised machine-learning process may be performed withrespect to a given set of parameters and/or categories of parametersthat have been suspected to be related to a given set of prognosticlabels, and/or are specified as linked to a medical specialty and/orfield of medicine covering a particular set of prognostic labels. As anon-limiting example, a particular set of blood test biomarkers and/orsensor 108 data may be typically used by cardiologists to diagnose orpredict various cardiovascular conditions, and a supervisedmachine-learning process may be performed to relate those blood testbiomarkers and/or sensor 108 data to the various cardiovascularconditions; in an embodiment, domain restrictions of supervisedmachine-learning procedures may improve accuracy of resulting models byignoring artifacts in training data. Domain restrictions may besuggested by experts and/or deduced from known purposes for particularevaluations and/or known tests used to evaluate prognostic labels.Additional supervised learning processes may be performed without domainrestrictions to detect, for instance, previously unknown and/orunsuspected relationships between physiological data and prognosticlabels.

With continued reference to FIG. 10 , machine-learning algorithms mayinclude unsupervised processes; unsupervised processes may, as anon-limiting example, be executed by an unsupervised learning module1004 executing on at least a server 104 and/or on another computingdevice in communication with at least a server 104, which may includeany hardware or software module. An unsupervised machine-learningprocess, as used herein, is a process that derives inferences indatasets without regard to labels; as a result, an unsupervisedmachine-learning process may be free to discover any structure,relationship, and/or correlation provided in the data. For instance, andwithout limitation, prognostic label learner 112 and/or at least aserver 104 may perform an unsupervised machine-learning process on firsttraining set 120, which may cluster data of first training set 120according to detected relationships between elements of the firsttraining set 120, including without limitation correlations of elementsof physiological state data 124 to each other and correlations ofprognostic labels to each other; such relations may then be combinedwith supervised machine-learning results to add new criteria forprognostic label learner 112 to apply in relating physiological statedata 124 to prognostic labels. As a non-limiting, illustrative example,an unsupervised process may determine that a first element ofphysiological data acquired in a blood test correlates closely with asecond element of physiological data, where the first element has beenlinked via supervised learning processes to a given prognostic label,but the second has not; for instance, the second element may not havebeen defined as an input for the supervised learning process, or maypertain to a domain outside of a domain limitation for the supervisedlearning process. Continuing the example a close correlation betweenfirst element of physiological state data 124 and second element ofphysiological state data 124 may indicate that the second element isalso a good predictor for the prognostic label; second element may beincluded in a new supervised process to derive a relationship or may beused as a synonym or proxy for the first physiological element byprognostic label learner 112.

Still referring to FIG. 10 , at least a server 104 and/or prognosticlabel learner 112 may detect further significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or categories of prognostic labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added to atleast a server 104, prognostic label learner 112 and/or at least aserver 104 may continuously or iteratively perform unsupervisedmachine-learning processes to detect relationships between differentelements of the added and/or overall data; in an embodiment, this mayenable at least a server 104 to use detected relationships to discovernew correlations between known biomarkers, prognostic labels, and/orameliorative labels and one or more elements of data in large bodies ofdata, such as genomic, proteomic, and/or microbiome-related data,enabling future supervised learning and/or lazy learning processes asdescribed in further detail below to identify relationships between,e.g., particular clusters of genetic alleles and particular prognosticlabels and/or suitable ameliorative labels. Use of unsupervised learningmay greatly enhance the accuracy and detail with which system may detectprognostic labels and/or ameliorative labels.

With continued reference to FIG. 10 , unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 10 , prognostic label learner 112 mayalternatively or additionally be designed and configured to generate atleast a prognostic output by executing a lazy learning process as afunction of the first training set 120 and the at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule 1008 executing on at least a server 104 and/or on anothercomputing device in communication with at least a server 104, which mayinclude any hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine-learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata prognostic label associated with biological extraction, using firsttraining set 120. As a non-limiting example, an initial heuristic mayinclude a ranking of prognostic labels according to relation to a testtype of at least a biological extraction, one or more categories ofphysiological data identified in test type of at least a biologicalextraction, and/or one or more values detected in at least a biologicalextraction; ranking may include, without limitation, ranking accordingto significance scores of associations between elements of physiologicaldata and prognostic labels, for instance as calculated as describedabove. Heuristic may include selecting some number of highest-rankingassociations and/or prognostic labels. Prognostic label learner 112 mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate prognosticoutputs as described in this disclosure, including without limitationlazy learning applications of machine-learning algorithms as describedin further detail below.

In an embodiment, and continuing to refer to FIG. 10 , prognostic labellearner 112 may generate a plurality of prognostic labels havingdifferent implications for a particular person. For instance, where theat least a biological extraction includes a result of a dexterity test,a low score may be consistent with amyotrophic lateral sclerosis,Parkinson's disease, multiple sclerosis, and/or any number of less severdisorders or tendencies associated with lower levels of dexterity. Insuch a situation, prognostic label learner 112 and/or at least a server104 may perform additional processes to resolve ambiguity. Processes mayinclude presenting multiple possible results to a medical practitioner,informing the medical practitioner that one or more follow-up testsand/or biological extractions are needed to further determine a moredefinite prognostic label. Alternatively or additionally, processes mayinclude additional machine-learning steps; for instance, where referenceto a model generated using supervised learning on a limited domain hasproduced multiple mutually exclusive results and/or multiple resultsthat are unlikely all to be correct, or multiple different supervisedmachine-learning models in different domains may have identifiedmutually exclusive results and/or multiple results that are unlikely allto be correct. In such a situation, prognostic label learner 112 and/orat least a server 104 may operate a further algorithm to determine whichof the multiple outputs is most likely to be correct; algorithm mayinclude use of an additional supervised and/or unsupervised model.Alternatively or additionally, prognostic label learner 112 may performone or more lazy learning processes using a more comprehensive set ofuser data to identify a more probably correct result of the multipleresults. Results may be presented and/or retained with rankings, forinstance to advise a medical professional of the relative probabilitiesof various prognostic labels being correct; alternatively oradditionally, prognostic labels associated with a probability ofcorrectness below a given threshold and/or prognostic labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an endocrinal test may determine that a givenperson has high levels of dopamine, indicating that a poor pegboardperformance is almost certainly not being caused by Parkinson's disease,which may lead to Parkinson's being eliminated from a list of prognosticlabels associated with poor pegboard performance, for that person.Similarly, a genetic test may eliminate Huntington's disease, or anotherdisease definitively linked to a given genetic profile, as a cause.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which additional processingmay be used to determine relative likelihoods of prognostic labels on alist of multiple prognostic labels, and/or to eliminate some labels fromsuch a list. Prognostic output 1012 may be provided to user outputdevice as described in further detail below.

With continued reference to FIG. 10 , prognostic label learner 112 maygenerate a plurality of prognostic outputs each containing a rankedprognostic probability score as a function of at least a biologicalextraction, at least a first training set, and at least a prognosticmachine-learning model. Prognostic probability score may include any ofthe prognostic probability scores as described above in reference toFIG. 1 . In an embodiment, prognostic label learner may rank pluralityof prognostic outputs in descending order of probability. In anembodiment, prognostic label learner 112 and/or at least a server mayselect at least a prognostic output as a function of prognosticprobability score. For example, prognostic label learner 112 and/or atleast a server may select at least a prognostic output having thehighest prognostic probability score. For example, prognostic labellearner 112 may generate a plurality of prognostic outputs for at leasta biological extraction such as a skin rash whereby a first prognosticoutput of hives may be associated with a prognostic probability score of92%, a second prognostic output of dermatitis may be associated with aprognostic probability score of 12%, and a third prognostic output ofLeprosy may be associated with a prognostic probability score of 0.5%.In such an instance, prognostic label learner 112 may select firstprognostic output of hives containing the highest prognostic probabilityscore to be included in at least a prognostic output and/or ameliorativeoutput.

With continued reference to FIG. 10 , prognostic label learner 112 maybe configured to select a prognostic machine-learning process as afunction of at least a biological extraction. Selecting a prognosticmachine-learning process may include selecting a machine-learning modelto generate a prognostic output and/or selecting an algorithm togenerate a machine-learning model. Selecting a prognosticmachine-learning process may be done utilizing any of the methodologiesas described above in reference to FIG. 1 .

Referring now to FIG. 11 , an exemplary embodiment of ameliorativeprocess label learner 144 is illustrated. Ameliorative process labellearner 144 may be configured to perform one or more supervised learningprocesses, as described above; supervised learning processes may beperformed by a supervised learning module 1100 executing on at least aserver 104 and/or on another computing device in communication with atleast a server 104, which may include any hardware or software module.For instance, a supervised learning algorithm may use prognostic labelsas inputs, ameliorative labels as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweenprognostic labels and ameliorative labels; scoring function may, forinstance, seek to maximize the probability that a given prognostic labeland/or combination of prognostic labels is associated with a givenameliorative label and/or combination of ameliorative labels to minimizethe probability that a given prognostic label and/or combination ofprognostic labels is not associated with a given ameliorative labeland/or combination of ameliorative labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain; for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofprognostic labels that have been suspected to be related to a given setof ameliorative labels, for instance because the ameliorative processescorresponding to the set of ameliorative labels are hypothesized orsuspected to have an ameliorative effect on conditions represented bythe prognostic labels, and/or are specified as linked to a medicalspecialty and/or field of medicine covering a particular set ofprognostic labels and/or ameliorative labels. As a non-limiting example,a particular set prognostic labels corresponding to a set ofcardiovascular conditions may be typically treated by cardiologists, anda supervised machine-learning process may be performed to relate thoseprognostic labels to ameliorative labels associated with varioustreatment options, medications, and/or lifestyle changes.

With continued reference to FIG. 11 , ameliorative process label learner144 may perform one or more unsupervised machine-learning processes asdescribed above; unsupervised processes may be performed by anunsupervised learning module 1104 executing on at least a server 104and/or on another computing device in communication with at least aserver 104, which may include any hardware or software module. Forinstance, and without limitation, ameliorative process label learner 144and/or at least a server 104 may perform an unsupervisedmachine-learning process on second training set 152, which may clusterdata of second training set 152 according to detected relationshipsbetween elements of the second training set 152, including withoutlimitation correlations of prognostic labels to each other andcorrelations of ameliorative labels to each other; such relations maythen be combined with supervised machine-learning results to add newcriteria for ameliorative process label learner 144 to apply in relatingprognostic labels to ameliorative labels. As a non-limiting,illustrative example, an unsupervised process may determine that a firstprognostic label 128 correlates closely with a second prognostic label156, where the first prognostic label 128 has been linked via supervisedlearning processes to a given ameliorative label, but the second hasnot; for instance, the second prognostic label 156 may not have beendefined as an input for the supervised learning process, or may pertainto a domain outside of a domain limitation for the supervised learningprocess. Continuing the example, a close correlation between firstprognostic label 128 and second prognostic label 156 may indicate thatthe second prognostic label 156 is also a good match for theameliorative label; second prognostic label 156 may be included in a newsupervised process to derive a relationship or may be used as a synonymor proxy for the first prognostic label 128 by ameliorative processlabel learner 144. Unsupervised processes performed by ameliorativeprocess label learner 144 may be subjected to any domain limitationssuitable for unsupervised processes performed by prognostic labellearner 112 as described above.

Still referring to FIG. 11 , at least a server 104 and/or ameliorativeprocess label learner 144 may detect further significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or categories of ameliorative labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added to atleast a server 104, ameliorative process label learner 144 and/or atleast a server 104 may continuously or iteratively perform unsupervisedmachine-learning processes to detect relationships between differentelements of the added and/or overall data; in an embodiment, this mayenable at least a server 104 to use detected relationships to discovernew correlations between known biomarkers, prognostic labels, and/orameliorative labels and one or more elements of data in large bodies ofdata, such as genomic, proteomic, and/or microbiome-related data,enabling future supervised learning and/or lazy learning processes toidentify relationships between, e.g., particular clusters of geneticalleles and particular prognostic labels and/or suitable ameliorativelabels. Use of unsupervised learning may greatly enhance the accuracyand detail with which system may detect prognostic labels and/orameliorative labels.

Continuing to view FIG. 11 , ameliorative process label learner 144 maybe configured to perform a lazy learning process as a function of thesecond training set 152 and the at least a prognostic output to producethe at least an ameliorative output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 112. Lazy learning processes may be performed by a lazy learningmodule 1108 executing on at least a server 104 and/or on anothercomputing device in communication with at least a server 104, which mayinclude any hardware or software module. Ameliorative output 1112 may beprovided to a user client device 188 and/or an advisor client device 184as described in further detail below.

In an embodiment, and still referring to FIG. 11 , ameliorative processlabel learner 144 may generate a plurality of ameliorative labels havingdifferent implications for a particular person. For instance, where aprognostic label indicates that a person has a magnesium deficiency,various dietary choices may be generated as ameliorative labelsassociated with correcting the deficiency, such as ameliorative labelsassociated with consumption of almonds, spinach, and/or dark chocolate,as well as ameliorative labels associated with consumption of magnesiumsupplements. In such a situation, ameliorative process label learner 144and/or at least a server 104 may perform additional processes to resolveambiguity. Processes may include presenting multiple possible results toa medical practitioner, informing the medical practitioner of variousoptions that may be available, and/or that follow-up tests, procedures,or counseling may be required to select an appropriate choice.Alternatively or additionally, processes may include additionalmachine-learning steps. For instance, ameliorative process label learner144 may perform one or more lazy learning processes using a morecomprehensive set of user data to identify a more probably correctresult of the multiple results. Results may be presented and/or retainedwith rankings, for instance to advise a medical professional of therelative probabilities of various ameliorative labels being correct orideal choices for a given person; alternatively or additionally,ameliorative labels associated with a probability of success orsuitability below a given threshold and/or ameliorative labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an additional process may reveal that a person isallergic to tree nuts, and consumption of almonds may be eliminated asan ameliorative label to be presented.

Continuing to refer to FIG. 11 , ameliorative process label learner 144may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 1116. As used herein,longitudinal data 1116 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,longitudinal data 1116 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 1116may related to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more ameliorative processes linked to one or more ameliorativeprocess labels. Ameliorative process label learner 144 may track one ormore elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenameliorative process over time on a physiological parameter. Functionsmay be compared to each other to rank ameliorative processes; forinstance, an ameliorative process associated with a steeper slope incurve representing improvement in a physiological data element, and/or ashallower slope in a curve representing a slower decline, may be rankedhigher than an ameliorative process associated with a less steep slopefor an improvement curve or a steeper slope for a curve marking adecline. Ameliorative processes associated with a curve and/or terminaldata point representing a value that does not associate with apreviously detected prognostic label may be ranked higher than one thatis not so associated. Information obtained by analysis of longitudinaldata 1116 may be added to ameliorative process database and/or secondtraining set.

With continued reference to FIG. 11 , ameliorative process label learner144 may be configured to generate a plurality of ameliorative outputseach containing a prognostic improvement score correlated to at least aprognostic output as a function of the at least a prognostic output, atleast a second training set, and at least an ameliorativemachine-learning model. Prognostic improvement score includes any of theprognostic improvement scores as described above in reference to FIG. 1. In an embodiment, ameliorative process label learner 144 may rankplurality of ameliorative outputs in descending order. For instance andwithout limitation, ameliorative process label learner 144 may generatea plurality of ameliorative outputs for a prognostic output such ashypertension which may include a first ameliorative output such as aubiquinol supplement containing a 67% prognostic improvement score, asecond ameliorative output such as a high blood pressure medicationcontaining a 28% prognostic improvement score, and a third ameliorativeoutput such as a yoga meditation sequence containing a 12% prognosticimprovement score. In an embodiment, ameliorative process label learner144 may select at least an ameliorative process label containing thehighest prognostic improvement score.

With continued reference to FIG. 11 , ameliorative process label learner144 may be configured to select an ameliorative machine-learning processas a function of a prognostic label. This may include for exampleselecting a machine-learning model and/or selecting an algorithm togenerate a machine-learning model. This may be done utilizing any of themethodologies as described above in reference to FIG. 1 .

Referring now to FIG. 12 , an exemplary embodiment of descriptorgenerator module 168 is illustrated. Descriptor generator module 168generates at least a descriptor trail 172 wherein the at least adescriptor trail 172 includes at least an element of diagnostic data176. Descriptor trail 172 includes any data and/or data elementdescribing generation and/or selection of at least a prognostic outputand/or ameliorative output including at least a prognostic output and atleast a correlated ameliorative output. Diagnostic data 176, includesany data and/or data element used to generate a prognostic output and/orameliorative output. Diagnostic data 176 may include at least abiological extraction, biological extraction classifier label, trainingdata, a first training set, a second training set, a prognostic output,an ameliorative output, a prognostic output and/or ameliorative output,any machine-learning model utilized to generate a prognostic outputand/or ameliorative output, a plurality of prognostic outputs, aplurality of ameliorative outputs, any regression models, weightedvariables, confidence levels, error functions, datasets, models, and/orcalculations utilized to generate a prognostic output and/orameliorative output as described above in more detail in reference toFIG. 1 .

With continued reference to FIG. 12 , descriptor generator module 168may include a label synthesizer 1204. In an embodiment, labelsynthesizer 1204 may be designed and configured to combine a pluralityof labels in at least a prognostic output together to provide maximallyefficient data presentation. Combination of labels together may includeelimination of duplicate information. For instance, label synthesizer1204 and/or at least a server 104 may be designed and configured todetermine a first prognostic label 128 of the at least a prognosticlabel is a duplicate of a second prognostic label 156 of the at least aprognostic label and eliminate the first prognostic label. Determinationthat a first prognostic label 128 is a duplicate of a second prognosticlabel 156 may include determining that the first prognostic label 128 isidentical to the second prognostic label; for instance, a prognosticlabel generated from test data presented in one biological extraction ofat least a biological extraction may be the same as a prognostic labelgenerated from test data presented in a second biological extraction ofat least a biological extraction. As a further non-limiting example, afirst prognostic label 128 may be synonymous with a second prognosticlabel, where detection of synonymous labels may be performed, withoutlimitation, by a language processing module 136 as described above.

Continuing to refer to FIG. 12 , label synthesizer 1204 may groupprognostic labels according to one or more classification systemsrelating the prognostic labels to each other. For instance, descriptorgenerator module 168 and/or label synthesizer 1204 may be configured todetermine that a first prognostic label 128 of the at least a prognosticlabel and a second prognostic label 156 of the at least a prognosticlabel belong to a shared category. A shared category may be a categoryof conditions or tendencies toward a future condition to which each offirst prognostic label 128 and second prognostic label 156 belongs; asan example, lactose intolerance and gluten sensitivity may each beexamples of digestive sensitivity, for instance, which may in turn sharea category with food sensitivities, food allergies, digestive disorderssuch as celiac disease and diverticulitis, or the like. Shared categoryand/or categories may be associated with prognostic labels as well. Agiven prognostic label may belong to a plurality of overlappingcategories. Descriptor generator module 168 may be configured to add acategory label associated with a shared category to prognostic outputand/or ameliorative output, where addition of the label may includeaddition of the label and/or a datum linked to the label, such as atextual or narrative description. In an embodiment, relationshipsbetween prognostic labels and categories may be retrieved from aprognostic label classification database 1208, for instance bygenerating a query using one or more prognostic labels of at least aprognostic output, entering the query, and receiving one or morecategories matching the query from the prognostic label classificationdatabase 1208.

With continued reference to FIG. 12 , descriptor generator module 168may be configured to generate descriptor trail 172 by converting one ormore elements of diagnostic data 176 into narrative language. As anon-limiting example, descriptor generator module 168 may include anarrative language unit 1212, which may be configured to determine anelement of narrative language associated with at least an element ofdiagnostic data 176 and include the element of narrative language indescriptor trail 172. Narrative language unit 1212 may implement this,without limitation, by using language processing module 136 to detectone or more associations between diagnostic data 176, lists ofdiagnostic data 176, and/or statements of narrative language. In anembodiment, descriptor generator module 168 may convert one or moreelements of diagnostic data 176 into narrative language such as forexample, a first training set, a plurality of prognostic outputs, atleast an ameliorative output, a confidence level calculation and thelike. Alternatively or additionally, narrative language unit 1212 mayretrieve one or more elements of narrative language from a narrativelanguage database 1216, which may contain one or more tables associatingdiagnostic data 176 and/or groups of diagnostic data 176 with words,sentences, and/or phrases of narrative language. One or more elements ofnarrative language may be included in descriptor trail 172, for instanceto display to an informed advisor as text describing an element oftraining data that was selected to generate a prognostic output.Descriptor trail 172 may include one or more images, one or more imagesmay be retrieved by descriptor generator module 168 from an imagedatabase, which may contain one or more tables associating diagnosticdata 176, groups of diagnostic data 176, or the like with one or moreimages. For example, descriptor trail 172 may include a biologicalextraction that includes an x-ray image or a prognostic output and/orameliorative output that includes a prognostic output that includes amagnetic resonance image (MRI).

With continued reference to FIG. 12 , label synthesizer 1204 may groupameliorative labels according to one or more classification systemsrelating the ameliorative labels to each other. For instance, labelsynthesizer 1104 may be configured to determine that a firstameliorative label of the at least an ameliorative label and a secondameliorative label of the at least an ameliorative label belong to ashared category. A shared category may be a category of conditions ortendencies toward a future condition to which each of first ameliorativelabel and second ameliorative label belongs; as an example, lactoseintolerance and gluten sensitivity may each be examples of digestivesensitivity, for instance, which may in turn share a category with foodsensitivities, food allergies, digestive disorders such as celiacdisease and diverticulitis or the like. In an embodiment, a firstameliorative label such as a yoga sequence and a second ameliorativelabel such as a cardiovascular exercise routine may each relate to ashared category such as fitness. In yet another non-limiting example, afirst ameliorative label such as a prescription medication and a secondameliorative label such as an oral supplement may each relate to ashared category such as oral therapies. Categories of ameliorativelabels and relationships between ameliorative labels may be retrievedfrom ameliorative label classification database 216 as described abovein more detail in reference to FIG. 6 . In an embodiment, descriptorgenerator module 168 may retrieve a category from ameliorative labelclassification database 216 by generating a query using one or moreameliorative labels generated by ameliorative label learner 160 andmatching the query from the ameliorative label classification database216.

With continued reference to FIG. 12 , descriptor generator module 168generate at least a descriptor trail 172 containing at least an elementof diagnostic data 176 by extracting at least an element of diagnosticdata 176 from descriptor trail data structure 140. Descriptor trail datastructure 140 may contain one or more tables each containing differentelements of diagnostic data 176 as described in more detail below.Descriptor generator module 168 may consult biological extractionclassifier database 700, physiological categories database 800, and/ortraining set database 900 to select other elements of diagnostic data176. In an embodiment, descriptor generator module 168 may retrieve atleast an element of diagnostic data 176 from a database such asdescriptor trail data structure 140 or physiological categories databaseby generating a query using one or more elements of prognostic outputand/or ameliorative output and matching the query to at least a dataentry contained within a database. Descriptor generator module 168 maybe in communication with advisor client device 184 to receive at leastan advisor filter input containing at least a diagnostic data 176selection and generate at least a descriptor trail 172 as a function ofthe at least an advisor filter. In an embodiment, at least an advisorfilter may be utilized to select at least an element of diagnostic data176 from a database. Descriptor generator module 168 may be configuredto transmit at least a descriptor trail 172 to at least an advisorclient device 184. This may be performed utilizing any networkmethodology as described herein.

Referring now to FIG. 13 , an exemplary embodiment of a prognostic labelclassification database 1208 is illustrated. Prognostic labelclassification database 1208 may be implemented as any database and/ordatastore suitable for use as biological extraction database 200 asdescribed above. One or more database tables in prognostic labelclassification database 1208 may include, without limitation, asymptomatic classification table 1300; symptomatic classification table1300 may relate each prognostic label to one or more categories ofsymptoms associated with that prognostic label. As a non-limitingexample, symptomatic classification table 1300 may include recordsindicating that each of lactose intolerance and gluten sensitivityresults in symptoms including gas buildup, bloating, and abdominal pain.One or more database tables in prognostic label classification database1208 may include, without limitation, a systemic classification table1304; systemic classification table 1304 may relate each prognosticlabel to one or more systems associated with that prognostic label. As anon-limiting example, systemic classification table 1304 may includerecords indicating each of lactose intolerance and gluten sensitivityaffects the digestive system; two digestive sensitivities linked toallergic or other immune responses may additionally be linked insystemic classification table 1304 to the immune system. One or moredatabase tables in prognostic label classification database 1208 mayinclude, without limitation, a body part classification table 1308; bodypart classification table 1308 may relate each prognostic label to oneor more body parts associated with that prognostic label. As anon-limiting example, body part classification table 1308 may includerecords indicating each of psoriasis and rosacea affects the skin of aperson. One or more database tables in prognostic label classificationdatabase 1208 may include, without limitation, a causal classificationtable 1312; causal classification table 1312 may relate each prognosticlabel to one or more causes associated with that prognostic label. As anon-limiting example, causal classification table 1312 may includerecords indicating each of type 2 diabetes and hypertension may haveobesity as a cause. The above-described tables, and entries therein, areprovided solely for exemplary purposes. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples for tables and/or relationships that may be includedor recorded in prognostic classification table consistently with thisdisclosure.

Referring now to FIG. 14 , and exemplary embodiment of a narrativelanguage database 1216 is illustrated. Narrative language database 1216may be implemented as any database and/or datastore suitable for use asbiological extraction database 200 as described above. One or moredatabase tables in narrative language database 1216 may include, withoutlimitation, a prognostic description table 1400, which may linkprognostic labels to narrative descriptions associated with prognosticlabels. One or more database tables in narrative language database 1216may include, without limitation, an ameliorative description table 1404,which may link ameliorative process labels to narrative descriptionsassociated with ameliorative process labels. One or more database tablesin narrative language database 1216 may include, without limitation, acombined description table 1408, which may link combinations ofprognostic labels and ameliorative labels to narrative descriptionsassociated with the combinations. One or more database tables innarrative language database 1216 may include, without limitation, aparagraph template table 1412, which may contain one or more templatesof paragraphs, pages, reports, or the like into which images and text,such as images obtained from image database 1220 and text obtained fromprognostic description table 1400, ameliorative description table 1404,and combined description table 1408 may be inserted. One or moredatabase tables in narrative language database 1216 may include, withoutlimitation, training set description table 1416, which may link trainingsets to narrative descriptions associated with training sets, such asphysiological state data 124 used in a training set. One or moredatabase tables in narrative language database 1216 may include, withoutlimitation, machine-learning model description table 1420, which maylink machine-learning models to narrative descriptions associated withmachine-learning models, such as narrative language describing aparticular hierarchical clustering model generated or a supervisedmachine-learning model that is generated. Other tables not illustratedin narrative language database 1216 may include for example biologicalextraction description table, a physiological state data descriptiontable, a prognostic output and/or ameliorative output table, and thelike. Tables in narrative language database 1216 may be populated, as anon-limiting example, using submissions from experts, which may becollected according to any processes described above. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various ways in which entries in narrative language database 1216 maybe categorized and/or organized.

Referring now to FIG. 15 , an exemplary embodiment of an image database1220 is illustrated. Image database 1220 may be implemented as anydatabase and/or datastore suitable for use as biological extractiondatabase 200 as described above. One or more database tables in imagedatabase 1220 may include, without limitation, a prognostic image table1500, which may link prognostic labels to images associated withprognostic labels. One or more database tables in image database 1220may include, without limitation, an ameliorative image table 1504, whichmay link ameliorative process labels to images associated withameliorative process labels. One or more database tables in imagedatabase 1220 may include, without limitation, a combined descriptiontable 1508, which may link combinations of prognostic labels andameliorative labels to images associated with the combinations. One ormore database tables in image database 102 may include, withoutlimitation, a prognostic video table 1512, which may link prognosticlabels to videos associated with prognostic labels. One or more databasetables in image database 1220 may include, without limitation, anameliorative video table 1516, which may link ameliorative processlabels to videos associated with ameliorative process labels. One ormore database tables in image database 1220 may include, withoutlimitation, a combined video table 1520, which may link combinations ofprognostic labels and ameliorative labels to videos associated with thecombinations. Other tables contained within image database 1220 and notillustrated may include for example biological extraction image table,machine-learning model image table, physiological state data 124 imagetable, a prognostic output and/or ameliorative output image table, andthe like. Tables in image database 1220 may be populated, withoutlimitation, by submissions by experts, which may be provided accordingto any process or process steps described in this disclosure forcollection of expert submissions.

Referring now to FIG. 16 , an exemplary embodiment of descriptor traildata structure 140 is illustrated. Descriptor trail data structure 140may be implemented as any database and/or datastore suitable use asbiological extraction database 200 as described above. One or moredatabase tables in descriptor trail data structure 140 may include,without limitation, biological extraction table 1600, which may includeany biological extraction utilized to generate at least a prognosticoutput and/or ameliorative output. One or more database tables indescriptor trail data structure 140 may include, without limitation,first training set table 1604, which may include any first training set120 utilized to generate at least a prognostic output and/orameliorative output. One or more database tables in descriptor traildata structure 140 may include, without limitation, prognosticmachine-learning model table 1608, which may include any prognosticmachine-learning model utilized to generate at least a prognostic outputand/or ameliorative output. One or more database tables in descriptortrail data structure 140 may include, without limitation, plurality ofameliorative outputs table 1612, which may include any plurality ofameliorative outputs generated by ameliorative label learner 160. One ormore database tables in descriptor trail data structure 140 may include,without limitation, confidence interval table 1616, which may includeany confidence level calculated and/or utilized to generate at least aprognostic output and/or ameliorative output. One or more databasetables in descriptor trail data structure 140 may include, withoutlimitation, prognostic output table 1620, which may include at least aprognostic output which may be included in prognostic output and/orameliorative output and/or selected from a plurality of prognosticoutputs generated by prognostic label learner 112. Tables in descriptortrail data structure 140 may be populated, without limitation, bysubmissions by at least a server 104, which may be designed andconfigured to record at least an element of diagnostic data 176.

Referring now to FIG. 17 , an exemplary embodiment of a method 1700 ofgenerating a descriptor trail 172 using artificial intelligence isillustrated. At step 1705 at least a server receives at least abiological extraction. Biological extraction includes any of thebiological extractions as described herein. At least a biologicalextraction may be received utilizing any methodology as describedherein. In an embodiment, at least a server 104 may be configured toreceive at least a biological extraction wherein the at least abiological extraction further comprises at least a fluid sample andclassify the at least a biological extraction. Classifying the at leasta biological extraction may include comparing the at least a biologicalextraction to at least a biological standard level and generating atleast a biological extraction classifier label. Biological standardlevel may include any of the biological standard levels as describedabove in reference to FIG. 1 . And FIG. 7 . Biological standard levelsmay be contained within biological extraction classifier label database700 as described above in more detail in reference to FIG. 7 . Forexample, at least a biological extraction such as a salivary estronelevel may be compared to a biological standard level contained withinbiological extraction classifier label database 700 and categorized as“normal” when salivary estrone level falls within standard level range.At least a biological extraction may be categorized according to anybiological extraction classification scheme, including any of thebiological extraction classifying schemes as described above inreference to FIG. 7 . In an embodiment, at least a biological extractionmay receive a plurality of biological extraction classifiers. Forinstance and without limitation, at least a biological extraction suchas a blood serum sample containing a troponin level of 0.5 nanograms permilliliter (ng/ml) as compared to a standard level retrieved frombiological extraction classifier label database 700 of 0 to 0.004nanograms per milliliter (ng/ml) may receive biological extractionclassifier label of “elevated label,” “abnormal findings label,” and “MDalert table” as any troponin level above 0.40 nanograms per milliliter(ng/ml) may be indicative of a myocardial infarction or heart attack.Generating at least a biological extraction classifier label may includematching a biological extraction with at least a category ofphysiological state data 124 received from at least an expert. In anembodiment, categories of physiological state data 124 may be containedwithin physiological categories database 800. Matching at least abiological extraction to at least a physiological category received fromat least an expert may include generating at least a query and matchingthe at least a query to an entry contained within physiologicalcategories database 800. For instance and without limitation, generatingat least a biological extraction classifier label may include matchingat least a biological extraction such as a saliva sample containing aprogesterone reading to a physiological category contained withinphysiological categories database 800 such as endocrine table 824. Inyet another non-limiting example, generating at least a biologicalextraction classifier label may include matching at least a biologicalextraction such as a stool sample containing a gut species analysis ofbacteria may be matched to a physiological category contained withinphysiological categories database 800 such as digestive table 808.

With continued reference to FIG. 17 , at step 1710 at least a server 104generates a prognostic output as a function of at least a biologicalextraction. Generating prognostic output includes selecting a prognosticmachine-learning process as a function of at least a biologicalextraction, recording the selected prognostic machine-learning processin a descriptor trail data structure, and generating the prognosticoutput using the selected prognostic machine-learning process as afunction of the at least a biological extraction. Selecting a prognosticmachine-learning process may include for example selecting a trainingset, selecting a machine-learning algorithm, and/or selecting amachine-learning model.

With continued reference to FIG. 17 , at step 1710 at least a server 104selects training data. Selecting training data may including selectingat least a first training set 120 wherein the at least a first trainingset 120 includes a plurality of first data entries, each first dataentry of the first training set 120 including at least an element ofphysiological state data 124 and at least a correlated first prognosticlabel. Physiological state data 124 may include any of the physiologicalstate data 124 as described above in reference to FIGS. 1-17 . Firstprognostic label 128 may include any of the first prognostic labels asdescribed above in reference to FIGS. 1-17 . In an embodiment, at leasta server 104 may select at least at least a first training set 120 as afunction of the at least a biological extraction classifier label. Forexample, at least a biological extraction classifier label that ismarked as “low” may be utilized to select at least a first training set120 that includes a plurality of first data entries where at least afirst data entry includes at least an element of physiological data thatincludes an element of “low” physiological data and at least acorrelated first prognostic label. In yet another non-limiting example,at least a biological extraction such as a blood sample containing afasting blood sugar level of 160 milligrams per deciliter (mg/dL) maycontain a biological extraction classifier label marked as “elevated.”In such an instance, biological extraction classifier label of“elevated” may be utilized to select at least a first training set 120that includes a plurality of first data entries where at least a dataentries includes at least an element of physiological data such as afasting blood glucose level of 185 milligrams per deciliter (mg/dL) andcorrelated to at least a first prognostic label 128 such aspre-diabetes. In an embodiment, at least a first training set 120 may beselected from training set database 900. At least a server 104 mayselect at least a first training set 120 as a function of the at least aphysically extracted sample contained within the at least a biologicalextraction. For example, at least a server 104 may determine category ofphysically extracted sample contained within at least a biologicalextraction such as a blood sample, a stool sample, a cerebrospinal fluidsample, and the like and match the physically extracted sample to acategory of training data organized by sample type contained withintraining set database 900. For instance and without limitation, at leasta biological extraction such as a stool sample may be matched to atraining set contained with digestive stool sample table 908. In yetanother non-limiting example, at least a biological extraction such as acerebrospinal fluid sample may be matched to at least a first trainingset 120 contained within neurological cerebrospinal fluid table 916.

With continued reference to FIG. 17 , generating at least a prognosticoutput may include creating at least a prognostic machine-learning modelrelating physiological state data 124 to prognostic labels using atleast a first training set 120 and generating the at least a prognosticoutput using the at least a biological extraction, the at least a firsttraining set, and the at least a prognostic machine-learning model. Atleast a prognostic output may be generated by prognostic label learner112. Prognostic machine-learning model includes any of themachine-learning models as described above in reference to FIGS. 1-17 .Prognostic label learner 112 may include supervised learning module,unsupervised learning module, and/or lazy learning module as describedabove in more detail in reference to FIG. 10 . Selecting a prognosticmachine-learning model may include selecting any of the machine-learningmodels as described above in reference to FIG. 10 . Prognostic labellearner 112 may generate a plurality of prognostic outputs eachcontaining a ranked prognostic probability score as a function of atleast a biological extraction, at least a first training set, and atleast a prognostic machine-learning model. Prognostic probability scoremay indicate a likelihood of a particular prognostic output associatedwith at least a biological extraction as described above in more detailin reference to FIG. 1 . In an embodiment, at least a prognostic outputselected to be included in at least a prognostic output and/orameliorative output may contained the highest prognostic probabilityscore indicating the highest likelihood of a particular biologicalextraction being associated with a particular prognosis. Prognosticlabel learner 112 may rank prognostic outputs whereby a plurality ofprognostic outputs may be generated and listed in decreasing order ofprobability. For example, prognostic label learner 112 may generate aplurality of prognostic output and/or ameliorative output for at least abiological extraction such as a fever whereby a first prognostic outputof flu may be associated with a prognostic probability score of 85%, asecond prognostic output of ear infection may be associated with aprognostic probability score of 22%, and a third prognostic output ofsepsis may be associated with a prognostic probability score of 1%. Insuch an instance, prognostic label learner 112 may select firstprognostic output of flu containing the highest prognostic probabilityscore to be included in at least a prognostic output and/or ameliorativeoutput.

With continued reference to FIG. 17 , at step 1715 at least a servergenerates an ameliorative output as a function of a prognostic output.Generating an ameliorative output includes selecting an ameliorativemachine-learning process as a function of the prognostic label,recording the selected ameliorative machine-learning process in thedescriptor trail data structure, and generating the ameliorative outputusing the selected ameliorative machine-learning process as a functionof the prognostic output. Selecting a prognostic machine-learningprocess may include selecting a training data set, selecting amachine-learning algorithm, and/or selecting a machine-learning model.

With continued reference to FIG. 17 , at least a server 104 and/orameliorative label learner may select at least a second training set 152as a function of the at least a first training set. At least a secondtraining set 152 includes a plurality of second data entries, eachsecond data entry of the second training set 152 including at least asecond prognostic label 156 and at least a correlated ameliorativeprocess label. At least a second training set 152 may be selected as afunction of the at least a first training set 120 as a function of adependency relationship between the two training sets. In an embodiment,at least a first training set 120 may be correlated to at least a secondtraining set 152 whereby first prognostic label 128 is utilized assecond prognostic label 156 to generate ameliorative process label 160.In an embedment, at least a first training set 120 may be stored with atleast a second training set 152 together in training set database 900 sothat selecting at least a first training set 120 automatically triggersselection of second training set 152 correlated and stored with thefirst training set 120 in training set database 900. Training setselection may be recorded by at least a server in descriptor trail datastructure.

With continued reference to FIG. 17 , generating at least anameliorative output may include creating at least an ameliorativemachine-learning model relating prognostic labels to ameliorative labelsusing at least a second training set 152 and generating the at least anameliorative output using the at least a second training set 152 and theat least a biological extraction. Ameliorative output may be generatedby ameliorative label learner 160. Ameliorative machine-learning modelincludes any of the machine-learning models as described above inreference to FIGS. 1-17 . Ameliorative label learner 160 may includesupervised learning module, unsupervised learning module, and/or lazylearning module as described above in more detail in reference to FIG.11 . Ameliorative label learner may be configured to generate aplurality of ameliorative outputs each containing a prognosticimprovement score correlated to at least a prognostic output as afunction of the at least a prognostic output, at least a second trainingset 152 and at least an ameliorative machine-learning model. Prognosticimprovement score indicates a likelihood of a particular ameliorativeoutput treating, preventing, and/or reversing a given prognostic output.For example, ameliorative label learner 160 may generate a plurality ofameliorative outputs for a prognostic output such as hypercholesteremiawhich may include a first ameliorative output such as a statinmedication containing a 52% prognostic improvement score, a secondameliorative output such as red rice yeast extract containing a 48%prognostic improvement score, and a third ameliorative output such ascardiovascular exercise three days each week containing a 22% prognosticimprovement score. In an embodiment, ameliorative label learner 160 mayrank ameliorative outputs in decreasing order of prognostic improvementscores and ameliorative label learner 160 may select at least anameliorative process label 160 from plurality of ameliorative processlabels to be included in prognostic output and/or ameliorative output.In an embodiment, ameliorative label learner 160 may select anameliorative output from plurality of ameliorative outputs containingthe highest prognostic improvement score.

With continued reference to FIG. 17 , at least a sever 104 may record atleast an element of diagnostic data 176. Diagnostic data 176 may includeany of the diagnostic data 176 as described above in reference to FIGS.1-17 . For example, at least a server 104 may record in memory forexample at least a first training set 120 utilized to generate at leasta prognostic output contained within a prognostic output and/orameliorative output. In yet another non-limiting example, at least aserver 104 may record in memory for example at least a biologicalextraction received from a user. In yet another non-limiting example, atleast a server 104 may record in memory a prognostic machine-learningmodel utilized to generate at least a prognostic output. In yet anothernon-limiting example, at least a server 104 may record in memory aplurality of ameliorative outputs generated by ameliorative labellearner 160.

With continued reference to FIG. 17 , at step 1720 at least a server 104generates at least a descriptor trail 172 wherein the descriptor trail172 includes at least an element of diagnostic data 176. Descriptortrail 172 includes any data and/or data element describing generationand/or selection of at least a prognostic output and/or ameliorativeoutput including at least a prognostic output and at least a correlatedameliorative output. Descriptor trail 172 may be generated by descriptorgenerator module 168 as described above in more detail in reference toFIG. 1 and FIG. 12 . Diagnostic data 176 contained within descriptortrail 172 may include any of the diagnostic data 176 as described abovein reference to FIGS. 1-17 . Generating at least a descriptor trail 172may include receiving at least an advisor filter input containing atleast a diagnostic data 176 selection, generating the at least adescriptor trail 172 as a function of the at least an advisor filter andtransmitting the at least a descriptor trail 172 to at least an advisorclient device. Advisor filter input as used herein includes any inputdatum received from at least an informed advisor containing aspecification for a particular element and/or elements of diagnosticdata 176. Advisor filter input includes any input datum received from atleast an informed advisor containing a specification for a particularelement and/or elements of diagnostic data 176 as described above inmore detail in reference to FIG. 1 . In an embodiment, advisor filterinput may be utilized to select diagnostic data 176 to be transmitted toat least an informed advisor. Advisor filter input may be received froman advisor client device. For example, advisor filter input may includean advisory input to only receive training data utilized to generate aplurality of prognostic outputs or an advisor filter input to receiveall diagnostic data 176 utilized to generate at least a prognosticoutput and/or ameliorative output. In an embodiment, advisor filterinput may include a preference to receive confidence levels andstatistical analysis utilized to generate a prognostic output and/orameliorative output. In yet another non-limiting example, advisor filterinput may include a request to receive prognostic probability scores orprognostic improvement scores.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 18 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1800 includes a processor 1804 and a memory1808 that communicate with each other, and with other components, via abus 1812. Bus 1812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 1808 may include various components (e.g., machine-readablemedia) including, but not limited to, a random access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1816 (BIOS), including basic routines thathelp to transfer information between elements within computer system1800, such as during start-up, may be stored in memory 1808. Memory 1808may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1800 may also include a storage device 1824. Examples ofa storage device (e.g., storage device 1824) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1824 may beconnected to bus 1812 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1824 (or one or more components thereof) may be removably interfacedwith computer system 1800 (e.g., via an external port connector (notshown)). Particularly, storage device 1824 and an associatedmachine-readable medium 1828 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1800. In one example,software 1820 may reside, completely or partially, withinmachine-readable medium 1828. In another example, software 1820 mayreside, completely or partially, within processor 1804.

Computer system 1800 may also include an input device 1832. In oneexample, a user of computer system 1800 may enter commands and/or otherinformation into computer system 1800 via input device 1832. Examples ofan input device 1832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1832may be interfaced to bus 1812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1812, and any combinations thereof. Input device 1832may include a touch screen interface that may be a part of or separatefrom display 1836, discussed further below. Input device 1832 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1800 via storage device 1824 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1840. A networkinterface device, such as network interface device 1840, may be utilizedfor connecting computer system 1800 to one or more of a variety ofnetworks, such as network 1844, and one or more remote devices 1848connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1844, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1820, etc.) may be communicated to and/or fromcomputer system 1800 via network interface device 1840.

Computer system 1800 may further include a video display adapter 1852for communicating a displayable image to a display device, such asdisplay device 1836. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1852 and display device 1836 maybe utilized in combination with processor 1804 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1800 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1812 via a peripheral interface 1856.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for generating a descriptor trail usingartificial intelligence the system comprising: at least a server, the atleast a server designed and configured to: receive at least a biologicalextraction; generate a prognostic output as a function of the at least abiological extraction, wherein the prognostic output is selected from aplurality of prognostic outputs, wherein generating the prognosticoutput further comprises: selecting a prognostic machine-learningprocess as a function of the at least a biological extraction; recordingthe selected prognostic machine-learning process in a descriptor traildata structure; and generating the prognostic output using the selectedprognostic machine-learning process as a function of the at least abiological extraction; generate an ameliorative output as a function ofthe prognostic output, wherein the ameliorative output is selected froma plurality of ameliorative outputs, wherein generating the ameliorativeoutput further comprises: selecting an ameliorative machine-learningprocess as a function of the prognostic output; recording the selectedameliorative machine-learning process in the descriptor trail datastructure; and generating the ameliorative output using the selectedameliorative machine-learning process as a function of the prognosticoutput; and a descriptor generator module operating on the at least aserver wherein the descriptor generator module is designed andconfigured to generate at least a descriptor trail from the descriptortrail data structure wherein the at least a descriptor trail furthercomprises at least an element of diagnostic data, wherein the diagnosticdata comprises a confidence interval associated with the prognosticoutput; and select a lazy-learning process as a function of the at leasta biological extraction; record the lazy-learning process in thedescriptor trail data structure; and generate the prognostic output andthe ameliorative output using the lazy-learning process as a function ofthe at least a biological extraction.
 2. The system of claim 1, wherein:the at least a biological extraction further comprises at least a fluidsample; and the at least a server is further configured to classify theat least a biological extraction wherein classifying the at least abiological extraction further comprises: comparing the at least abiological extraction to at least a biological standard level; andgenerating at least a biological extraction classifier label.
 3. Thesystem of claim 2, wherein generating the at least a biologicalextraction classifier label further comprises matching the at least abiological extraction with at least a category of physiological statedata received from at least an expert.
 4. The system of claim 2, whereinselecting the prognostic machine-learning process as a function of theat least a biological extraction further comprises selecting at least afirst training set as a function of the at least a biological extractionclassifier label.
 5. The system of claim 1, wherein selecting theprognostic machine-learning process further comprises selecting at leasta first training set as a function of at least a physically extractedsample contained within the at least a biological extraction.
 6. Thesystem of claim 1, wherein the at least a server is further configuredto: generate the plurality of prognostic outputs each containing aranked prognostic probability score; record the plurality of prognosticoutputs in the descriptor trail data structure; and generate theprognostic output as a function of the ranked prognostic probabilityscore.
 7. The system of claim 1, wherein the at least a server isfurther configured to: generate a plurality of ameliorative outputs eachcontaining a prognostic improvement score correlated to at least aprognostic output as a function of the at least a prognostic output;record the plurality of ameliorative outputs in the descriptor traildata structure; and generate the ameliorative output as a function ofthe prognostic improvement score.
 8. The system of claim 1, wherein theat least a server is further configured to: select a lazy-learningprocess as a function of the at least a biological extraction; recordthe lazy-learning process in the descriptor trail data structure; andgenerate the ameliorative output using the lazy-learning process as afunction of the at least a biological extraction.
 9. The system of claim1, wherein the descriptor generator module is further configured to:receive at least an advisor filter input containing at least adiagnostic data selection; generate the at least a descriptor trail as afunction of the at least an advisor filter; and transmit the at least adescriptor trail to at least an advisor client device.
 10. A method ofgenerating a descriptor trail using artificial intelligence the methodcomprising: receiving by at least a server at least a biologicalextraction; generating by the at least a server a prognostic output as afunction of the at least a biological extraction, wherein the prognosticoutput is selected from a plurality of prognostic outputs, whereingenerating the prognostic output further comprises: selecting aprognostic machine-learning process as a function of the at least abiological extraction; recording the selected prognosticmachine-learning process in a descriptor trail data structure; andgenerating the prognostic output using the selected prognosticmachine-learning process as a function of the at least a biologicalextraction; generating by the at least a server an ameliorative outputas a function of the prognostic output, wherein the ameliorative outputis selected from a plurality of ameliorative outputs, wherein generatingthe ameliorative output further comprises: selecting an ameliorativemachine-learning process as a function of the prognostic output;recording the selected ameliorative machine-learning process in thedescriptor trail data structure; and generating the ameliorative outputusing the selected ameliorative machine-learning process as a functionof the prognostic output; and generating by the at least a server atleast a descriptor trail from the descriptor trail data structurewherein the at least a descriptor trail further comprises at least anelement of diagnostic data, wherein the diagnostic data comprises aconfidence interval associated with the prognostic output; and selectinga lazy-learning process as a function of the at least a biologicalextraction; recording the lazy-learning process in the descriptor traildata structure; and generating the prognostic output and theameliorative output using the lazy-learning process as a function of theat least a biological extraction.
 11. The method of claim 10, whereinreceiving at least a biological extraction further comprises: receivingat least a at least a fluid sample; and classifying the at least abiological extraction wherein classifying the at least a biologicalextraction further comprises: comparing the at least a biologicalextraction to at least a biological standard level; and generating atleast a biological extraction classifier label.
 12. The method of claim11, wherein generating the at least a biological extraction classifierlabel further comprises matching the at least a biological extractionwith at least a category of physiological state data received from atleast an expert.
 13. The method of claim 11, wherein selecting aprognostic machine-learning process further comprises selecting at leasta first training set as a function of the at least a biologicalextraction classifier label.
 14. The method of claim 10, whereinselecting a prognostic machine-learning process further comprisesselecting at least a first training set as a function of at least aphysically extracted sample contained within the at least a biologicalextraction.
 15. The system of claim 10, wherein generating theprognostic output further comprises: generating a plurality ofprognostic outputs each containing a ranked prognostic probabilityscore; recording the plurality of prognostic outputs in the descriptortrail data structure; and generating the prognostic output as a functionof the ranked prognostic probability score.
 16. The method of claim 10,wherein generating an ameliorative output further comprises: generatinga plurality of ameliorative outputs each containing a prognosticimprovement score correlated to at least a prognostic output as afunction of the at least a prognostic output; recording the plurality ofameliorative outputs in the descriptor trail data structure; andgenerating the ameliorative output as a function of the prognosticimprovement score.
 17. The method of claim 10, wherein generating anameliorative output further comprises: selecting a lazy-learning processas a function of the at least a biological extraction; recording thelazy-learning process in the descriptor trail data structure; andgenerating the ameliorative output using the lazy-learning process as afunction of the at least a biological extraction.
 18. The method ofclaim 10, wherein generating at least a descriptor trail furthercomprises: receiving at least an advisor filter input containing atleast a diagnostic data selection; generating the at least a descriptortrail as a function of the at least an advisor filter; and transmittingthe at least a descriptor trail to at least an advisor client device.